Remote medical data acquisition method and system based on 5G network

Abstract

The invention discloses a remote medical data acquisition method and system based on a 5G network, and the method comprises the steps: collecting 5G network state information in real time, calculating a network state information monitoring index, can dynamically adjust a data transmission strategy, and guarantees the timely and safe transmission of key medical data under a complex network condition; meanwhile, a cache and bandwidth cooperation factor and an intelligent cache strategy factor are introduced, the cache depth and bandwidth allocation are dynamically adjusted according to the network state and the medical data priority, intelligent cooperation management of bandwidth and cache is achieved, and continuity of medical data transmission is guaranteed; the constructed early warning model can detect network abnormity in time and trigger alarm, important data are processed preferentially according to an alarm level adjustment strategy, and the abnormity processing and data recovery capability is improved.

Description

Technical Field

[0001] The present invention relates to the field of data collection and network transmission technology, and in particular to a remote medical data collection method and system based on a 5G network. Background Art

[0002] With the rapid development of information technology, 5G network has become one of the important innovations in the field of modern communications due to its high bandwidth, low latency and wide coverage. The application of 5G technology is not limited to personal communications, but also shows broad prospects in fields such as the Internet of Things, intelligent transportation, industrial automation, etc. In the medical field, the introduction of 5G network provides strong support for the further development of telemedicine technology. Through the 5G network, the real-time collection and transmission of medical data can be realized. Especially in high-precision applications such as remote monitoring, remote diagnosis and remote surgery, the application of 5G network has greatly improved the efficiency and quality of medical services. Although 5G network has significant advantages in telemedicine, how to ensure the transmission quality and reliability of medical data in an unstable network environment is still an important technical challenge currently faced.

[0003] Existing telemedicine data collection methods mainly rely on traditional network architectures and data transmission protocols, and fail to fully utilize the advantages of 5G networks, especially in dealing with complex network fluctuations and diverse medical data needs. Specifically, the existing system's real-time monitoring and analysis of network status information is not accurate enough, and there is a lack of dynamic adjustment mechanism for information monitoring indicators under network status, resulting in low data transmission efficiency and stability. Secondly, when responding to the transmission needs of emergency medical data, the existing system often fails to reasonably allocate bandwidth resources, which easily leads to delays or loss of critical data. In addition, the existing systems generally lack intelligent caching and bandwidth coordination mechanisms, and cannot effectively utilize cache resources when the network fluctuates or is congested to ensure the continuity and integrity of data transmission. Summary of the invention

[0004] The purpose of this section is to summarize some aspects of embodiments of the present invention and briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section and the specification abstract and the invention title of this application to avoid blurring the purpose of this section, the specification abstract and the invention title, and such simplifications or omissions cannot be used to limit the scope of the present invention.

[0005] In view of the above existing problems, the present invention is proposed. Therefore, the present invention provides a remote medical data collection method based on a 5G network to solve the problems mentioned in the background technology.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0007] In a first aspect, the present invention provides a remote medical data collection method based on a 5G network, comprising:

[0008] Collect the status information and medical data of the 5G network in real time, calculate the current network status information monitoring indicators through the status information, analyze the historical status information of the 5G network, and determine the normal fluctuation range and limit value of different information monitoring indicators under the network status;

[0009] Setting a medical data priority for the medical data, and allocating bandwidth for each type of medical data in turn according to the current network status information monitoring index and the medical data priority;

[0010] Set cache and bandwidth coordination factors, and adjust intelligent cache strategy factors based on each network status information monitoring indicator and medical data priority;

[0011] A medical data collection early warning model is constructed, and medical data exception processing and data recovery are performed based on the cache and bandwidth coordination factor and the intelligent cache strategy factor.

[0012] As a preferred solution of the remote medical data collection method based on 5G network described in the present invention, the state information and medical data of the 5G network are collected in real time, and the current network state information monitoring index is calculated through the state information, including:

[0013] According to the number of monitoring indicators of network status information, the weight coefficient of each monitoring indicator is determined, and the previous moment of the current moment is taken as the sliding average to perform weighted summation of the network status information to obtain the current network status information monitoring indicator.

[0014] As a preferred solution of the 5G network-based telemedicine data collection method described in the present invention, the historical status information of the 5G network is analyzed to determine the normal fluctuation range and limit values ​​of different information indicators under the network status, including:

[0015] By setting a time decay factor, which decreases with time, and re-assigning the weight of the current network status information according to the time decay factor, a historical network status information monitoring indicator is obtained;

[0016] The current network status information monitoring index and the historical network status information monitoring index are combined to obtain the coefficient of variation of the network status information. The normal fluctuation range and limit value of different information monitoring indicators under the network status are determined by the coefficient of variation.

[0017] As a preferred solution of the remote medical data collection method based on 5G network described in the present invention, wherein: setting medical data priority for the medical data, and allocating bandwidth for each type of medical data in turn according to the current network status information monitoring index and the medical data priority, including:

[0018] Determine the data type and urgency of each type of medical data according to the medical data, and obtain the priority of each type of medical data according to the data type and urgency;

[0019] Considering the current network status information monitoring indicators and the priority of medical data, the priority weight of medical data is obtained.

[0020] As a preferred solution of the 5G network-based remote medical data collection method described in the present invention, it also includes:

[0021] Bandwidth is allocated for each type of medical data according to the priority weight, and the higher the priority weight, the greater the proportion of allocated bandwidth.

[0022] As a preferred solution of the 5G network-based remote medical data collection method described in the present invention, the cache and bandwidth coordination factors are set, and the intelligent cache strategy factors are adjusted according to the network status information monitoring indicators and medical data priority each time, including:

[0023] The initial value of the cache and bandwidth coordination factor is obtained based on each network status information monitoring indicator and medical data priority, and the cache and bandwidth coordination factor is adjusted according to the real-time network fluctuation and the urgency of the medical data;

[0024] According to the cache and bandwidth coordination factor and the current network status information monitoring index, the intelligent cache strategy factor is obtained, and the intelligent cache strategy factor is adjusted through the adjusted cache and bandwidth coordination factor.

[0025] As a preferred solution of the remote medical data collection method based on 5G network described in the present invention, a medical data collection early warning model is constructed, and abnormal processing and data recovery of medical data are performed according to the cache and bandwidth coordination factor and the intelligent cache strategy factor, including:

[0026] Calculate the standard deviation and average value of the cache and bandwidth coordination factor and the intelligent cache strategy factor, establish a medical data collection alarm threshold, and set an alarm level according to the alarm threshold;

[0027] If it is found that the cache and bandwidth coordination factor or the intelligent cache strategy factor exceeds the alarm threshold, an alarm is triggered, and the current status of the cache and bandwidth coordination factor and the intelligent cache strategy factor is monitored in real time according to the alarm level until the cache and bandwidth coordination factor and the intelligent cache strategy factor are both lower than the alarm threshold, and data recovery is performed.

[0028] In a second aspect, the present invention provides a remote medical data acquisition system based on a 5G network, which includes:

[0029] The network status information monitoring module is configured to collect the status information and medical data of the 5G network in real time, calculate the current network status information monitoring index through the status information, analyze the historical status information of the 5G network, and determine the normal fluctuation range and limit value of different information monitoring indicators under the network status;

[0030] A medical data transmission module is configured to set a medical data priority for the medical data, and allocate bandwidth for each type of medical data in turn according to the current network status information monitoring index and the medical data priority;

[0031] The cache and bandwidth allocation coordination module is configured to set cache and bandwidth coordination factors, and adjust intelligent cache strategy factors according to each network status information monitoring indicator and medical data priority;

[0032] The medical data collection warning module is configured to build a medical data collection warning model, and perform exception processing and data recovery on the medical data according to the cache and bandwidth coordination factor and the intelligent cache strategy factor.

[0033] In a third aspect, the present invention provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, wherein: the processor implements any step of the above method when executing the computer program.

[0034] In a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: the computer program implements any step of the above method when executed by a processor.

[0035] Compared with the prior art, the invention has the following beneficial effects:

[0036] 1. The present invention can dynamically adjust the data transmission strategy by collecting the status information of the 5G network in real time and calculating the network status information monitoring indicators. This mechanism effectively copes with the dynamic fluctuations in the 5G network and significantly improves the real-time and stability of the system for medical data transmission;

[0037] 2. By introducing cache and bandwidth coordination factors and intelligent cache strategy factors, the present invention realizes intelligent coordination of bandwidth resources and cache management. It can dynamically adjust bandwidth allocation and cache strategy according to real-time network status and priority of medical data. When the network fluctuates or is congested, it can relieve bandwidth pressure by increasing cache depth and gradually release cache data after network recovery, ensuring the continuity and integrity of data transmission. Even in a complex network environment, the system can ensure the stable transmission of important medical data.

[0038] 3. By building a medical data collection early warning model, it is possible to detect and take countermeasures in a timely manner when the network is abnormal. By setting the alarm threshold and alarm level, when the cache and bandwidth coordination factor or the intelligent cache strategy factor exceeds the threshold, it can quickly trigger the warning and adjust the strategy to protect important data from loss. In addition, the early warning model can give priority to processing and transmitting key data after the network is restored to ensure the security and integrity of the data, greatly improving the system's response and recovery capabilities in the event of sudden network anomalies. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. Among them:

[0040] Figure 1 This is an overall flow chart of a remote medical data collection method based on a 5G network according to an embodiment of the present invention;

[0041] Figure 2 This is a comparative diagram of data transmission stability analysis of different methods of a remote medical data collection method based on a 5G network according to an embodiment of the present invention;

[0042] Figure 3 This is a comparison chart of the robustness of different methods of remote medical data collection based on 5G network in fault recovery according to an embodiment of the present invention. DETAILED DESCRIPTION

[0043] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0044] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0045] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0046] The present invention is described in detail with reference to schematic diagrams. When describing the embodiments of the present invention, for the sake of convenience, the cross-sectional diagrams showing the device structure will not be partially enlarged according to the general scale, and the schematic diagrams are only examples, which should not limit the scope of protection of the present invention. In addition, in actual production, the three-dimensional dimensions of length, width and depth should be included.

[0047] At the same time, in the description of the present invention, it should be noted that the directions or positional relationships indicated by the terms "upper, lower, inner and outer" are based on the directions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific direction, be constructed and operated in a specific direction, and therefore cannot be understood as limiting the present invention. In addition, the terms "first, second or third" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0048] In the present invention, unless otherwise clearly specified and limited, the terms "install, connect, connect" should be understood in a broad sense, for example: it can be a fixed connection, a detachable connection or an integral connection; it can also be a mechanical connection, an electrical connection or a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0049] Example 1

[0050] Reference Figure 1 , which is the first embodiment of the present invention, and provides a remote medical data collection method based on a 5G network, comprising:

[0051] S1. Collect the status information and medical data of 5G network in real time, calculate the current network status information monitoring indicators through the status information, analyze the historical status information of 5G network, and determine the normal fluctuation range and limit value of different information monitoring indicators under the network status;

[0052] Further, according to the number of monitoring indicators of the network status information, the weight coefficient of each monitoring indicator is determined, and the previous moment of the current moment is taken as the sliding average, and the weighted sum of the network status information is performed to obtain the current network status information monitoring indicator;

[0053] Specifically, the network status information includes bandwidth utilization, latency, and packet loss rate;

[0054] Among them, the network status information monitoring indicator is the dynamic change rate;

[0055] Furthermore, the number and weight coefficients of the monitoring indicators are determined by the parameters included in the network status information. For example, if the network status information includes bandwidth utilization, delay, and packet loss rate, then the corresponding parameters are 3, and the weight coefficients corresponding to the parameters are also 3.

[0056] It should be explained that for the sake of simplicity, the solution only considers three parameters: bandwidth utilization, delay, and packet loss rate. These parameters can be added or deleted in actual applications.

[0057] Specifically, the process formula for obtaining the network status information monitoring indicator is:

[0058] NetFlux t =α×BU t +β×Latency t +γ×PLR t +NetFlux t-1

[0059] Among them, α is the bandwidth utilization weight, β is the delay weight, γ is the packet loss rate weight, BU t is the bandwidth utilization at the current time t, Latency t is the delay at the current time t, PLR t is the packet loss rate at the current time t, NetFlux t is the network status information monitoring indicator at the current time t, NetFlux t-1 is the network status information monitoring indicator at the previous time t-1 of the current time t, expressed as a sliding average;

[0060] It should be noted that the formula takes into account the instantaneous state and recent state of the network, where the time from time t to time t-1 is an approximate value. This method not only takes into account the currently collected data, but also takes into account the data fluctuations in the recent period of time, and obtains a more stable network status indicator by smoothing short-term fluctuations.

[0061] Furthermore, by setting a time decay factor that decreases with time, and re-assigning the weight of the current network status information according to the time decay factor, a historical network status information monitoring indicator is obtained;

[0062] Specifically, the process formula for obtaining the historical network status information monitoring indicator is as follows:

[0063]

[0064] Among them, ω i is the time decay factor, which decreases as time i increases, that is, more weight is given to recent data; NetFlux i is the historical i-th network status information monitoring indicator value, and n is the time window size of the historical data (which can be set to a few hours, a few days, or even longer);

[0065] It should be noted that when analyzing historical status information, we do not simply take the historical average value, but set the time decay factor to give more weight to recent data, and gradually decay the weight of older data in historical data; at the same time, the formula retains the long-term trend of the network status, but focuses on the recent data behavior, providing a more timely and effective basis for determining the normal fluctuation range and limit value;

[0066] Furthermore, the current network status information monitoring index and the historical network status information monitoring index are combined to obtain the coefficient of variation of the network status information, and the normal fluctuation range and limit value of different information monitoring indicators under the network status are determined by the coefficient of variation;

[0067] Specifically, the coefficient of variation CV is calculated as follows:

[0068]

[0069] Among them, σ represents the standard deviation and μ represents the mean;

[0070] Specifically, according to the coefficient of variation CV, the normal fluctuation range of different information monitoring indicators under the network state is expressed as:

[0071]

[0072] Specifically, the limit values ​​of different information monitoring indicators under the network state are obtained as follows:

[0073]

[0074] Specifically, NetFlux can be t Keep it within a reasonable range;

[0075] It should be noted that the coefficient of variation can be used to quantify the fluctuation of the network status, and the threshold value can be set by combining the standard deviation and mean of the current network status information monitoring indicators and the historical network status information monitoring indicators, thus avoiding the limitation that a single average value cannot reflect the real fluctuation of the network, and enhancing the system's sensitivity and responsiveness to abnormal situations;

[0076] S2. Set medical data priority for medical data, and allocate bandwidth for each type of medical data in turn according to the current network status information monitoring indicators and medical data priority;

[0077] Further, according to the medical data, the data type and urgency of each type of medical data are determined, and the priority of each type of medical data is obtained through the data type and urgency;

[0078] Specifically, data types include,electrocardiograms, MRI images, real-time videos, etc., which are prioritized according to their importance to,the patient's health;

[0079] Specifically, the urgency level includes the degree of real-time requirements, the doctor’s instructions, the patient’s current status, etc.;

[0080] Specifically, the priority of each type of medical data is expressed as:

[0081] MP i =BP i +AF i

[0082] Among them, BP i is the data type of the i-th medical data, AF i is the urgency of the i-th medical data, MP i is the priority of the i-th type of medical data;

[0083] Furthermore, the priority weight of medical data is obtained by considering the current network status information monitoring indicators and the priority of medical data;

[0084] Specifically, the priority weight of medical data is expressed as:

[0085] PW=MP i ×(1+NetFlux t )

[0086] Specifically, when the network status is good (NetFlux t When the network status is poor (NetFlux t High), high priority medical data (high MP i ) will get bandwidth first because of the larger PW;

[0087] Furthermore, bandwidth is allocated for each type of medical data according to the priority weight, and the higher the priority weight, the greater the proportion of allocated bandwidth;

[0088] Specifically, the bandwidth is allocated as follows:

[0089]

[0090] Among them, BW i is the bandwidth obtained for the i-th medical data, TBW is the total bandwidth of the current 5G network, ∑ j PW j is the sum of the priority weights of all medical data;

[0091] S3. Set cache and bandwidth coordination factors, and adjust the intelligent cache strategy factors according to each network status information monitoring indicator and medical data priority;

[0092] Furthermore, the initial value of the cache and bandwidth coordination factor is obtained based on each network status information monitoring indicator and medical data priority, and the cache and bandwidth coordination factor is adjusted according to the real-time network fluctuation and the urgency of the medical data;

[0093] Specifically, the cache and bandwidth coordination factor SC is expressed as:

[0094]

[0095] Specifically, when the network status is poor (NetFlux t When the network status is good (NetFlux t When the bandwidth is low, the SC value is large, indicating that the system can use the bandwidth more flexibly for data transmission;

[0096] It should be explained that since SC is a synergistic factor used to adjust the coordination between bandwidth and cache, it will affect the BW allocation strategy, especially when the network status is unstable or bandwidth resources are limited; therefore, when SC is high, a certain type of medical data will obtain more bandwidth resources for transmission, and the corresponding BW value will increase; when SC is low, a certain type of medical data will rely on cache for temporary storage, reducing bandwidth allocation, and the corresponding BW value will decrease;

[0097] Specifically, the process of adjusting the cache and bandwidth coordination factor SC is as follows:

[0098] ΔSC t =SC t ×(1+θ×ΔNetFlux)

[0099] Among them, θ is the adjustment coefficient used to control the dynamic amplitude of SC, and ΔNetFlux is the NetFlux at the current moment. t and NetFlux at the previous moment t-1 The amount of change;

[0100] It should be noted that the adjustment process not only considers the fluctuation of the current network status information, but also combines the priority of medical data, and realizes the adaptive optimization of cache and bandwidth by dynamically adjusting SC;

[0101] Furthermore, according to the cache and bandwidth coordination factor and the current network status information monitoring index, the intelligent cache strategy factor is obtained, and the intelligent cache strategy factor is adjusted by the adjusted cache and bandwidth coordination factor;

[0102] Specifically, the intelligent cache strategy factor CS is expressed as:

[0103]

[0104] Among them, BCS is the basic cache strategy factor, which represents the cache strategy strength of the system under ideal conditions;

[0105] Similarly, the intelligent cache strategy factor CS is adjusted as follows:

[0106] ΔCS=CS×(1+λ×ΔSC)

[0107] Among them, λ is the adjustment coefficient, which is used to control the change speed of CS; ΔSC is the SC at the current moment t and the previous moment SC t-1 The amount of change;

[0108] Specifically, when SC becomes larger, CS will reduce the cache depth, at which point BW is high and NetFlux is low, and the system relies more on bandwidth transmission. Conversely, when SC becomes smaller, CS will increase the cache depth, at which point BW is low and NetFlux is high, reducing the reliance on network bandwidth. When SC is extremely small (near 0), CS is extremely high (always growing), at which point CS is extremely low and NetFlux is extremely high, and the system relies almost entirely on the cache, waiting for network conditions to improve before recovering and transmitting data.

[0109] It should be noted that through the coordinated adjustment of SC and CS, the system can achieve the best combination of bandwidth and cache under complex network conditions, and ensure the effective transmission and storage of medical data even in the case of large network fluctuations;

[0110] S4. Build a medical data collection early warning model to handle medical data exceptions and recover data based on cache and bandwidth coordination factors and intelligent cache strategy factors;

[0111] Further, the standard deviation and average value of the cache and bandwidth coordination factor and the intelligent cache strategy factor are calculated, the medical data collection alarm threshold is established, and the alarm level is set according to the alarm threshold;

[0112] Specifically, the medical data collection alarm threshold is expressed as:

[0113] AT=SC σ ×SC μ +CS σ ×CS μ

[0114] Among them, SC σ , SC μ is the standard deviation and mean of the cache-bandwidth coordination factor, CS σ , CS μ is the standard deviation and mean of the smart cache strategy factor;

[0115] Furthermore, if it is found that the cache and bandwidth synergy factor or the intelligent cache strategy factor exceeds the alarm threshold, an alarm is triggered, and according to the alarm level, the current status of the cache and bandwidth synergy factor and the intelligent cache strategy factor is monitored in real time until the cache and bandwidth synergy factor and the intelligent cache strategy factor are both below the alarm threshold, and data recovery is performed;

[0116] Specifically, the alarm levels are divided as follows:

[0117] Low warning: SC or CS is equal to AT, the system will temporarily increase the cache depth to relieve network load, and compress or delay the transmission of low-priority data;

[0118] Medium warning: SC or CS is slightly higher than AT. The system performs redundant backup of important medical data to prevent data loss. At the same time, bandwidth allocation will give priority to high-priority medical data.

[0119] High warning: When SC or CS significantly exceeds AT, the system enters emergency mode, suspends the transmission of non-critical data, concentrates all bandwidth resources to ensure the transmission of the most important data, and restores this data first after the network is restored;

[0120] It should be noted that during the alarm process, the system not only considers the priority of the data, but also performs intelligent recovery based on the marking information during the warning, avoiding the impact of data peaks after network recovery, while ensuring that critical medical data can be transmitted in a timely manner.

[0121] Furthermore, this embodiment also provides a remote medical data collection system based on a 5G network, including:

[0122] The network status information monitoring module is configured to collect the status information and medical data of the 5G network in real time, calculate the current network status information monitoring index through the status information, analyze the historical status information of the 5G network, and determine the normal fluctuation range and limit value of different information monitoring indicators under the network status;

[0123] A medical data transmission module is configured to set a medical data priority for the medical data, and allocate bandwidth for each type of medical data in turn according to the current network status information monitoring index and the medical data priority;

[0124] The cache and bandwidth allocation coordination module is configured to set cache and bandwidth coordination factors, and adjust intelligent cache strategy factors according to each network status information monitoring indicator and medical data priority;

[0125] The medical data collection warning module is configured to build a medical data collection warning model, and perform exception processing and data recovery on the medical data according to the cache and bandwidth coordination factor and the intelligent cache strategy factor.

[0126] This embodiment further provides a computer device, which is applicable to the remote medical data collection method based on a 5G network, including:

[0127] Memory and processor; the memory is used to store computer-executable instructions, and the processor is used to execute computer-executable instructions to implement the remote medical data collection method based on the 5G network as proposed in the above embodiment.

[0128] The computer device may be a terminal, and the computer device includes a processor, a memory, a communication interface, a display screen and an input device connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device may be a touch layer covering the display screen, or a key, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse, etc.

[0129] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, the remote medical data collection method based on the 5G network proposed in the above embodiment is implemented.

[0130] The storage medium proposed in this embodiment and the data storage method proposed in the above embodiment belong to the same inventive concept. The technical details not fully described in this embodiment can be found in the above embodiment, and this embodiment has the same beneficial effects as the above embodiment.

[0131] Example 2

[0132] Reference Figure 2 and Figure 3 , which is the second embodiment of the present invention, and this embodiment provides a remote medical data collection method based on a 5G network, including: In order to verify the effectiveness of a remote medical data collection method based on a 5G network, especially the data transmission efficiency and stability under complex network conditions, the following simulation experiment was conducted, the purpose of the simulation experiment is to compare the performance of the method of the present invention with the prior art in processing an unstable network environment, so as to prove the advantages of the present invention when the network fluctuation is large;

[0133] The experiment was conducted in a simulated 5G network environment, with different fluctuation ranges (such as network delay, packet loss rate, bandwidth utilization, etc.). The experimental environment was set up as follows:

[0134] 5G network simulator, used to simulate the real 5G network environment, supports multiple frequency bands and network protocols. In the experiment, the simulator sets different network delays (10ms to 200ms), packet loss rates (0.1% to 5%), and bandwidth utilization (50Mbps to 1Gbps) to simulate the real environment under different network conditions;

[0135] Edge computing nodes, as network edge nodes, simulate the processing and caching of medical data in an edge computing environment;

[0136] Network status monitoring equipment, used to monitor the status information of 5G networks (delay, packet loss rate, bandwidth utilization, etc.) in real time and generate monitoring indicators for experimental data analysis;

[0137] Medical data collection equipment, which simulates the actual medical data collection process, including electrocardiogram, MRI images, real-time video, etc., and transmits data through the 5G network;

[0138] Data storage device, supporting multi-protocol storage and high-speed caching functions, used to store and cache medical data;

[0139] Routers and switches connect 5G network simulators and edge computing nodes to ensure the stability of data transmission;

[0140] The experiment designed three comparison groups:

[0141] Control group A (traditional method): adopts the existing telemedicine data collection method, uses fixed bandwidth allocation and caching strategy, and does not consider the changes in real-time network status;

[0142] Experimental group B (improved method): dynamic bandwidth allocation and cache strategy are adopted, without considering the weight distribution during medical data transmission;

[0143] Experimental group C (method of the present invention): based on experimental group B, the priority weight of medical data is considered and an early warning is set;

[0144] During the experiment, the key parameters of each group were collected, including data transmission delay, packet loss rate, bandwidth utilization, cache depth, etc. At the same time, the data transmission success rate and system response time of each group during network fluctuations were recorded to obtain the average values ​​of various indicators, as shown in Table 1;

[0145] Table 1 Statistical results of each method under the average value

[0146]

[0147] First, from the data analysis in Table 1, the method of the present invention shows significant advantages in various key parameters. Compared with the control group A, the data transmission delay of the experimental group B and the experimental group C is reduced by 20% and 33.3%, respectively, indicating that the present invention is more effective in reducing the delay of data transmission when dealing with real-time network fluctuations, especially in the transmission of high-priority medical data, showing higher timeliness; in addition, the reduction in packet loss rate also proves the effectiveness of the method of the present invention. The packet loss rate of the experimental group B and the experimental group C is reduced by 40% and 60% respectively compared with the control group A, indicating that the present invention effectively ensures the integrity of the data and reduces the dynamic adjustment of the intelligent caching strategy and bandwidth coordination factor. Data loss due to network instability; in terms of bandwidth utilization, experimental groups B and C adjusted the bandwidth allocation strategy in real time, which increased the bandwidth utilization by 5% and 13.5% compared with control group A; this shows that under the same network conditions, the method of the present invention can transmit more data and further improve the efficiency of medical data transmission; finally, the adjustment of cache depth enables the system to effectively relieve bandwidth pressure and ensure the continuity of data transmission when the network fluctuates or is congested. The cache depth of experimental groups B and C increased by 20% and 40% respectively compared with control group A, which further explains why the present invention can still maintain a high data transmission success rate under complex network conditions;

[0148] Secondly, combined Figure 2It can be seen that the data of control group A has been in an unstable state during the data transmission process, while the data of experimental groups B and C are in a stable state during the transmission process. However, the fluctuation of experimental group B is more obvious than that of experimental group C, which means that experimental group B did not consider the problem of weight distribution during data transmission; Figure 3 The present invention respectively places the above three simulation experiments into the system to test its recovery ability in case of failure. It is found that the robustness of the system of the present invention is 6% to 8% higher than that of other systems, indicating that the system of the present invention can still maintain functional continuity and performance stability when encountering a failure, ensuring that the system can continue to operate under fault conditions and avoiding the loss of medical data during transmission. It fully verifies that the method of the present invention can not only improve the efficiency of data transmission, but also effectively deal with fluctuations in 5G networks and failures in actual transmission, and has strong practical application value.

[0149] Those skilled in the art will appreciate that the embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the application can adopt the form of complete hardware embodiments, complete software embodiments, or embodiments in combination with software and hardware. Moreover, the application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program codes. The scheme in the embodiments of the present application can be implemented in various computer languages, for example, object-oriented programming language Java and literal scripting language JavaScript, etc.

[0150] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0151] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1A function specified in one or more boxes.

[0152] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0153] Although the preferred embodiments of the present application have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications falling within the scope of the present application.

[0154] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is also intended to include these modifications and variations.

Claims

1. A remote medical data collection method based on 5G network, characterized in that: include: Collect the status information and medical data of the 5G network in real time, calculate the current network status information monitoring indicators through the status information, analyze the historical status information of the 5G network, and determine the normal fluctuation range and limit value of different information monitoring indicators under the network status; Setting a medical data priority for the medical data, and allocating bandwidth for each type of medical data in turn according to the current network status information monitoring index and the medical data priority; Set cache and bandwidth coordination factors, and adjust intelligent cache strategy factors based on each network status information monitoring indicator and medical data priority; A medical data collection early warning model is constructed, and medical data exception processing and data recovery are performed based on the cache and bandwidth coordination factor and the intelligent cache strategy factor.

2. The remote medical data collection method based on 5G network as claimed in claim 1, characterized in that: Collect the status information and medical data of the 5G network in real time, and calculate the current network status information monitoring indicators through the status information, including: According to the number of monitoring indicators of network status information, the weight coefficient of each monitoring indicator is determined, and the previous moment of the current moment is taken as the sliding average to perform weighted summation of the network status information to obtain the current network status information monitoring indicator.

3. The remote medical data collection method based on 5G network as claimed in claim 2, characterized in that: Analyze the historical status information of the 5G network and determine the normal fluctuation range and limit values ​​of different information indicators under the network status, including: By setting a time decay factor, which decreases with time, and re-assigning the weight of the current network status information according to the time decay factor, a historical network status information monitoring indicator is obtained; The current network status information monitoring index and the historical network status information monitoring index are combined to obtain the coefficient of variation of the network status information. The normal fluctuation range and limit value of different information monitoring indicators under the network status are determined by the coefficient of variation.

4. The remote medical data collection method based on 5G network as claimed in claim 3, characterized in that: Setting a medical data priority for the medical data, and allocating bandwidth for each type of medical data in turn according to the current network status information monitoring index and the medical data priority, including: Determine the data type and urgency of each type of medical data according to the medical data, and obtain the priority of each type of medical data according to the data type and urgency; Considering the current network status information monitoring indicators and the priority of medical data, the priority weight of medical data is obtained.

5. The remote medical data collection method based on 5G network as claimed in claim 4, characterized in that: Also includes: Bandwidth is allocated for each type of medical data according to the priority weight, and the higher the priority weight, the greater the proportion of allocated bandwidth.

6. The remote medical data collection method based on 5G network as claimed in claim 5, characterized in that: Set cache and bandwidth coordination factors, and adjust intelligent cache strategy factors based on each network status information monitoring indicator and medical data priority, including: The initial value of the cache and bandwidth coordination factor is obtained based on each network status information monitoring indicator and medical data priority, and the cache and bandwidth coordination factor is adjusted according to the real-time network fluctuation and the urgency of the medical data; According to the cache and bandwidth coordination factor and the current network status information monitoring index, the intelligent cache strategy factor is obtained, and the intelligent cache strategy factor is adjusted through the adjusted cache and bandwidth coordination factor.

7. The remote medical data collection method based on 5G network as claimed in claim 6, characterized in that: Construct a medical data collection early warning model, and perform abnormal processing and data recovery on medical data according to the cache and bandwidth coordination factor and the intelligent cache strategy factor, including: Calculate the standard deviation and average value of the cache and bandwidth coordination factor and the intelligent cache strategy factor, establish a medical data collection alarm threshold, and set an alarm level according to the alarm threshold; If it is found that the cache and bandwidth coordination factor or the intelligent cache strategy factor exceeds the alarm threshold, an alarm is triggered, and the current status of the cache and bandwidth coordination factor and the intelligent cache strategy factor is monitored in real time according to the alarm level until the cache and bandwidth coordination factor and the intelligent cache strategy factor are both lower than the alarm threshold, and data recovery is performed.

8. A 5G network-based telemedicine data collection system, based on the 5G network-based telemedicine data collection method according to any one of claims 1 to 7, characterized in that: include: The network status information monitoring module is configured to collect the status information and medical data of the 5G network in real time, calculate the current network status information monitoring index through the status information, analyze the historical status information of the 5G network, and determine the normal fluctuation range and limit value of different information monitoring indicators under the network status; A medical data transmission module is configured to set a medical data priority for the medical data, and allocate bandwidth for each type of medical data in turn according to the current network status information monitoring index and the medical data priority; The cache and bandwidth allocation coordination module is configured to set cache and bandwidth coordination factors, and adjust intelligent cache strategy factors according to each network status information monitoring indicator and medical data priority; The medical data collection warning module is configured to build a medical data collection warning model, and perform exception processing and data recovery on the medical data according to the cache and bandwidth coordination factor and the intelligent cache strategy factor.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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