Intelligent medical system based on 5G remote communication

Abstract

The invention provides an intelligent medical system based on 5G remote communication, and belongs to the technical field of data processing systems. The system comprises a first acquisition module used for acquiring an instant medical request initiated by a first terminal to a second terminal; the establishing module is used for responding to the instant medical treatment request and establishing a communication channel between the first terminal and the second terminal through the dispatching center; the encryption module is used for dynamically encrypting the communication channel in a fragmentation manner in combination with the information entropy of the data to be transmitted in the communication channel; the communication module is used for carrying out a communication task about the instant medical request through the encrypted communication channel; the second acquisition module is used for acquiring communication channel delay in the communication task process; the compensation module is used for carrying out smooth compensation on communication channel delay through a Kalman filtering algorithm in combination with a variational algorithm; and the return module is used for calling the second acquisition module until the communication task is terminated. And the real-time performance and the safety of medical communication are improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of data processing systems, and specifically relates to a smart medical system based on 5G remote communication. Background Art

[0002] 5G remote communication is a communication method that uses the fifth generation of mobile communication technology to achieve high speed, low latency and large-scale device connection. In the medical field, 5G can be used for remote surgery, real-time monitoring, rapid sharing of patient data and other scenarios, greatly improving medical efficiency and coverage. Smart healthcare is a medical service model based on information technology (such as the Internet of Things, artificial intelligence, and big data). It integrates and analyzes medical resources to achieve intelligent and efficient medical services.

[0003] Smart healthcare solves the problems of uneven distribution of medical resources and inefficient management of chronic diseases by combining 5G communications, artificial intelligence and IoT technologies. In the context of an aging society and an increase in the number of patients with chronic diseases, smart healthcare can provide convenient remote diagnosis and treatment, efficient health monitoring and intelligent diagnosis, which can help alleviate medical pressure, reduce costs and improve patient experience.

[0004] However, the existing remote intelligent medical process is easily affected by the network status, and the encryption scheme is also a traditional encryption method with poor anti-interference ability. Communication delays often occur, affecting the communication quality and real-time performance of instant communication tasks. Severe cases may lead to medical accidents, restricting the further development of telemedicine. Summary of the invention

[0005] In order to solve the technical problems that the existing remote intelligent medical process in the prior art is easily affected by the network status, the encryption scheme is also a traditional encryption method, the anti-interference ability is poor, and communication delays are prone to frequent occurrence, which affects the communication quality and real-time performance of instant communication tasks, and may seriously cause medical accidents, thus restricting the further development of telemedicine, the present invention provides a smart medical system based on 5G remote communication.

[0006] The present invention provides a smart medical system based on 5G remote communication, the smart medical system comprising a first terminal, a dispatching center, and a second terminal connected in sequence;

[0007] The smart medical system based on 5G remote communication also includes:

[0008] A first acquisition module, used to acquire an instant medical treatment request initiated by the first terminal to the second terminal;

[0009] An establishing module, configured to establish a communication channel between the first terminal and the second terminal through a dispatch center in response to an immediate medical treatment request;

[0010] An encryption module, used to dynamically encrypt the communication channel in a fragmented manner in combination with the information entropy of the data to be transmitted in the communication channel;

[0011] A communication module, used for performing communication tasks regarding immediate medical treatment requests through an encrypted communication channel;

[0012] A second acquisition module is used to acquire the communication channel delay during the communication task;

[0013] A compensation module is used to smoothly compensate for the communication channel delay by combining a variational algorithm with a Kalman filter algorithm;

[0014] The return module is used to call the second acquisition module until the communication task is terminated.

[0015] Compared with the prior art, the present invention has at least the following beneficial technical effects:

[0016] In the present invention, the data of remote communication is encrypted by a sharding method combined with the information entropy of the data to be transmitted. The key can be dynamically generated according to the complexity of the data, the randomness and security of the encryption can be improved, and the single point leakage can be prevented by sharding storage, the anti-attack capability can be enhanced, and the integrity and privacy of data transmission can be ensured. During the communication process, the communication channel delay is collected in real time, the variational algorithm is combined, the communication channel delay is smoothly compensated by the Kalman filter algorithm, the communication channel delay is compensated by the variational algorithm and the Kalman filter algorithm, and the target path of the delay compensation is optimized by the variational algorithm to ensure that the delay changes are smooth and optimal; Kalman filtering fuses the observed value and the predicted value on this basis to improve the dynamic adaptability and accuracy of the delay change. The combination of the two can reduce the violent fluctuations and maintain the real-time and stability of the compensation. Avoid the frequent compensation strategy caused by delay fluctuations, so that even if the network delay fluctuates, the system is adjusted very gently, and other communication channels will not be disconnected due to the sudden allocation of excessive bandwidth in the same communication channel, saving network resources while ensuring the communication process, and maintaining the sustainability and stability of the entire smart medical process. Improving the data security of medical communications and the fluency of instant communications, reducing communication delays, and making medical communications with extremely high real-time requirements suitable for complex and changeable network scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of 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.

[0018] Figure 1It is a structural schematic diagram of a smart medical system based on 5G remote communication provided by the present invention;

[0019] Figure 2 It is a structural schematic diagram of another smart medical system based on 5G remote communication provided by the present invention. DETAILED DESCRIPTION

[0020] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the specific implementation methods of the present invention will be described below with reference to the accompanying drawings. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings and other implementation methods can be obtained based on these drawings without creative work.

[0021] In order to simplify the drawings, only the parts related to the invention are schematically shown in each figure, and they do not represent the actual structure of the product. In addition, in order to simplify the drawings and facilitate understanding, in some figures, only one of the parts with the same structure or function is schematically drawn or marked. In this article, "one" not only means "only one", but also means "more than one".

[0022] It should be further understood that the term "and / or" used in the present description and the appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0023] In this document, it should be noted that, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection. It can be a mechanical connection or an electrical connection. It can be directly connected or indirectly connected through an intermediate medium, and 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.

[0024] In addition, in the description of the present invention, the terms "first", "second", etc. are only used to distinguish the description and cannot be understood as indicating or implying relative importance.

[0025] In one embodiment, referring to Figure 1 , showing a schematic diagram of the structure of a smart medical system based on 5G remote communication provided by the present invention. Figure 2 , showing a structural schematic diagram of another smart medical system based on 5G remote communication provided by the present invention.

[0026] The present invention provides a smart medical system based on 5G remote communication, and the smart medical system includes a first terminal, a dispatching center, and a second terminal connected in sequence.

[0027] Among them, the first terminal and the second terminal can be both patient-side devices, doctor-side devices, both patient-side devices, or both doctor-side devices. When the first terminal and the second terminal are both patient-side devices, the first terminal collects the patient's health data and shares it with the second terminal through the dispatch center. When the first terminal is a patient-side device and the second terminal is a doctor-side device, the patient initiates a video consultation request through a smart phone (first terminal), and the doctor receives and provides a diagnosis through a computer (second terminal). When the first terminal is a doctor-side device and the second terminal is a patient-side device, the doctor actively contacts the patient or monitors the remote medical device. When the first terminal and the second terminal are both doctor-side devices, doctors collaborate or consult with each other. The dispatch center is the core hub of the system, responsible for resource scheduling, communication channel establishment, security management and data transfer. It can be understood that such a design makes the system highly flexible and adaptable, and can meet the needs of various scenarios such as data sharing between patients, interaction between doctors and patients, remote monitoring, and collaboration between doctors. At the same time, the dispatch center uniformly manages resources, establishes communication channels and ensures data security, which improves the versatility, scalability and medical service efficiency of the system.

[0028] The smart medical system based on 5G remote communication also includes:

[0029] The first acquisition module 1 is used to acquire an instant medical treatment request initiated by a first terminal to a second terminal.

[0030] Among them, an instant medical request refers to a real-time medical service request initiated by the first terminal (such as the patient-side device) to the second terminal (such as the doctor-side device), including video consultation, voice consultation, vital sign data access, etc. It requires the rapid establishment of a communication channel to meet real-time requirements.

[0031] In a possible implementation, the instant medical treatment request includes a video request, a voice request, and a request for access to patient vital sign monitoring data.

[0032] Establishing module 2 is used to establish a communication channel between the first terminal and the second terminal through the dispatch center in response to the immediate medical treatment request.

[0033] In a possible implementation, the establishment module 2 is specifically used for:

[0034] When the instant medical treatment request is a video request or a voice request, a communication channel is established through the WebRTC protocol or the RTP protocol.

[0035] When the instant medical request is a request for access to patient vital sign monitoring data, a communication channel is established through the MQTT protocol or the HTTP / 2 protocol.

[0036] It should be noted that the system flexibly selects the communication protocol according to the type of immediate medical request: video or voice requests use WebRTC or RTP protocols to ensure low-latency real-time transmission. Vital sign monitoring data requests use MQTT or HTTP / 2 protocols to support efficient and lightweight data transmission, thereby meeting the needs of different types of communication tasks and improving system adaptability and performance.

[0037] The encryption module 3 is used to dynamically encrypt the communication channel in a fragmented manner in combination with the information entropy of the data to be transmitted in the communication channel.

[0038] Among them, the data to be transmitted refers to various medical information that needs to be transmitted in the communication channel, including patients' vital signs monitoring data, diagnostic records, audio and video streams, etc. Information entropy is an indicator to measure the complexity and randomness of data. The larger the value, the more complex the data and the higher the randomness. The fragmentation method is to divide the data to be transmitted into multiple small fragments, and each fragment is encrypted and stored independently. By combining information entropy to perform fragmented encryption on the data to be transmitted, encryption keys can be dynamically generated, and the encryption strength can be adjusted according to the complexity of the data to enhance data security and anti-attack capabilities. Sharded storage prevents single point failure. Even if part of the key or data is leaked, the complete information cannot be restored, thereby improving the privacy of medical communications and system reliability.

[0039] In a possible implementation manner, the encryption module 3 is specifically used for:

[0040] Get the data to be transmitted.

[0041] Calculate the information entropy value of the data to be transmitted:

[0042]

[0043] in, represents the information entropy value of the data D to be transmitted, represents the probability of occurrence of the i-th symbol in the data to be transmitted, , n represents the total number of symbol types in the data to be transmitted, and log represents the logarithmic function;

[0044] Generate dynamic keys based on information entropy value:

[0045]

[0046] in, represents the random seed, Represents a dynamic key, Represents the exclusive OR operation;

[0047] Shard the dynamic key and store it in a distributed hash table:

[0048]

[0049] in, represents the jth dynamic key shard, represents the jth sharding parameter, N represents the upper limit of the sharding parameter, express The hash value of express Distributed hash table node, mod represents the modulus operation;

[0050] Each shard is stored separately, and the complete key can only be restored after reorganization, preventing single point failure or key leakage.

[0051] Shard the data to be transmitted based on the number of shards of the dynamic key:

[0052]

[0053] in, Indicates the i-th data fragment to be transmitted, , m represents the total number of data fragments to be transmitted, Indicates the total length of data to be transmitted. express Total length;

[0054] The dynamic key obtained by sharding is used to encrypt the data to be transmitted obtained by each shard:

[0055]

[0056] in, Indicates based on The encryption algorithm, Express The i-th data fragment obtained after encryption;

[0057] Among them, the encryption algorithm is based on dynamic key sharding The encryption algorithm uses each key shard to encrypt the corresponding data shard. Even if a key shard is leaked, the complete data cannot be cracked. Optionally, the encryption algorithm can be AES (Advanced Encryption Standard), ChaCha20, or SM4 that complies with the national encryption standard, etc.

[0058] The data to be transmitted is transmitted in an encrypted manner to complete the encryption of the communication channel.

[0059] It should be noted that the key is dynamically generated by combining information entropy calculation, and the encryption strength is adjusted according to the complexity of the data. The key is stored in shards and managed in a distributed hash table to prevent single point failure and leakage. Through shard encryption, each fragment of the transmitted data is encrypted separately. Even if part of the key is leaked, the complete data cannot be restored, further improving the security and anti-attack capabilities of the transmission, ensuring encryption performance and standard compliance, and achieving comprehensive encryption protection of the communication channel.

[0060] In a possible implementation manner, the encryption module 3 is further used for:

[0061] When the receiving end receives the encrypted data to be transmitted, each dynamic key fragment is reassembled to obtain a dynamic key, wherein the receiving end includes a first terminal and a second terminal.

[0062] The encrypted data to be transmitted is decrypted using the reorganized dynamic key, and the data to be transmitted is restored at the receiving end.

[0063] It should be noted that the encryption module restores the complete key by reorganizing the dynamic key fragments at the receiving end to ensure the security and reliability of key recovery. The encrypted data is decrypted using the reorganized key to successfully restore the data to be transmitted, thereby achieving end-to-end data protection. Even if some fragments are leaked, the data cannot be cracked, further improving communication security and privacy.

[0064] The communication module 4 is used to perform communication tasks regarding immediate medical treatment requests through an encrypted communication channel.

[0065] It is understandable that the task of executing instant medical requests through encrypted communication channels ensures the security and integrity of patient vital data, audio and video streams and other information during transmission, while meeting real-time requirements and improving the efficiency and privacy protection of telemedicine services.

[0066] The second acquisition module 5 is used to acquire the communication channel delay during the communication task.

[0067] Among them, communication channel delay refers to the time interval required for data to be transmitted from the first terminal (such as the patient end) to the second terminal (such as the doctor end). The delay is determined by many factors, such as network transmission speed, communication protocol efficiency, data encryption and decryption time, etc. By obtaining the communication channel delay in real time, the system can dynamically monitor network performance, ensure the real-time transmission of medical data and audio and video streams, and provide a basis for delay compensation and resource optimization, effectively improving the stability and response speed of telemedicine services.

[0068] The compensation module 6 is used to smoothly compensate the communication channel delay by combining the variational algorithm with the Kalman filter algorithm.

[0069] Among them, the variational algorithm is a mathematical optimization method used to find the optimal path or value of a function. In communication delay compensation, the variational algorithm is used to impose smoothing constraints on the delay prediction value to avoid drastic fluctuations and ensure that the compensation strategy is more gentle and stable. Kalman filtering is a recursive estimation algorithm that combines the predicted value and the observed value to dynamically adjust the state estimate. In delay compensation, Kalman filtering is used to correct the delay prediction value and balance the credibility of real-time observation data and historical prediction models. By combining the variational algorithm and the Kalman filter algorithm to smoothly compensate for the communication channel delay, the delay prediction path can be optimized, unnecessary adjustments caused by sudden fluctuations can be reduced, and the dynamic adaptability to delay changes can be enhanced to ensure the stability of communication and efficient use of resources.

[0070] It should be noted that sometimes the network's predicted delay changes can be particularly drastic, such as predicting a very low delay one second and a very high delay the next. The predicted value may be suddenly high or low (for example, it suddenly jumps from 10ms to 50ms and then back to 10ms), and the system will adjust frequently, causing instability. Unsteady prediction results can easily lead to unnecessary compensation operations. Variational processing makes the predicted delay value smoother, avoiding drastic changes while maintaining accurate compensation.

[0071] By using the variational method and adding "smooth control", the system can be more gentle when predicting delay changes, avoiding sudden and drastic fluctuations. The system will give priority to a smoother predicted path, and even if the delay changes, it will gradually adjust, like slowly braking. Through this smoothing process, even if the network delay fluctuates, the system will adjust gently, and will not suddenly disconnect or waste network resources (that is, if the network bandwidth resources are limited, and the predicted delay value fluctuates too much: the system may occupy too many resources in a short period of time, resulting in a lack of bandwidth for other tasks (such as video streaming, audio streaming), triggering congestion management. In extreme cases, the system may be judged as an unstable connection and interrupted, such as VoIP services or streaming media will actively disconnect unstable connections), increasing the stability and fluency of remote communications.

[0072] In a possible implementation manner, the compensation module 6 is specifically used for:

[0073] S601: Determine a constraint function of a predicted value of a communication channel delay by using a variational method.

[0074] In a possible implementation, the constraint function is specifically:

[0075]

[0076] in, represents the constraint function value, represents the predicted value at time k, express The true value of represents the initial time of integration, Indicates the end time of integration, represents the regularization parameter that controls the importance of smoothness, represents partial derivative, Represents the Lagrangian of the constraint function.

[0077] Specifically, if the observed value contains more random fluctuations or noise, take the larger , suppressing the dramatic changes in the predicted value. For example: in a highly volatile network environment, the system needs to maintain a stable delay prediction. ≈0.5 or higher. If the observed value is relatively stable, the system can rely more on the prediction model and choose a smaller , improve the sensitivity of the prediction. For example: in an environment where the delay changes slowly, set =0.05.

[0078] Among them, in the constrained optimization problem, the Lagrangian combines the objective function and the constraints to find the solution to the optimization problem. By deriving the Lagrangian, we can find the point where the objective function reaches the extreme value under the constraints. In the constraint function of delay prediction, the Lagrangian represents the balance between the error term and smoothness of delay prediction. By constructing the constraint function, the communication delay prediction value is optimized to balance the prediction accuracy and smoothness. Regularization parameter Importance of adjusting smoothing control: When the observed data fluctuates greatly, increase Suppresses drastic changes in predicted values. When the delay is stable, reduce Improve prediction sensitivity so that it can dynamically adapt to different network environments and ensure prediction stability and accuracy.

[0079] S602: With the goal of minimizing the constraint function value, determine the predicted value of the communication channel delay through the Kalman filter algorithm.

[0080] In a possible implementation manner, the predicted value of the communication channel delay is calculated as follows:

[0081]

[0082] Among them, min means taking the minimum value, represents the predicted value at time k+1 based on the state at time k, A represents the state transition matrix of how the current state affects the state at the next moment, Representation and The relevant k-time control input is the bandwidth adjustment amount, and B represents the control input A matrix of control inputs that affect the strength of the predicted values.

[0083] It should be noted that the delay prediction value is dynamically calculated through the state transfer matrix A and the control input matrix B, and the delay prediction result is optimized by combining the impact of the current state on the next moment and the control input (such as bandwidth adjustment amount). It is used to suppress drastic delay fluctuations and ensure the stability of the predicted path. By minimizing the constraint function value, the system balances the predicted and actual delay changes while ensuring accuracy, improving the real-time and stability of communication.

[0084] S603: Introduce network noise to correct the Kalman gain used to balance the predicted value weight and the observed value weight and the error covariance used to evaluate the credibility of the predicted value, wherein the network noise includes process noise introduced due to the inability to describe the real noise and measurement noise caused by noise equipment errors.

[0085] In a possible implementation, the Kalman gain for balancing the predicted value weight and the observed value weight and the error covariance for evaluating the credibility of the predicted value are specifically:

[0086]

[0087] in, represents the Kalman gain at time k, that is, the corrected Kalman gain, Represents the observed value at time k The observation matrix of the mapping relationship between the real value and the real value, the subscript T indicates the transpose, and Q indicates the process noise including the k-time The process noise covariance matrix, R represents the measurement noise at time k The measurement noise covariance matrix is represents the stability factor, Represents the error covariance with time k The associated forecast error covariance represents the uncertainty of the forecast value at time k+1, represents the corrected error covariance, I represents the unit matrix, represents the initial process noise, represents the initial measurement noise, represents the dynamic adjustment factor of the weight at time k, represents the process noise at time k, represents the measurement noise, The weight factor that controls the impact of noise.

[0088] Optionally, the stability factor takes a value of 0 to 0.2 under stable network delay, a value of 0.5 to 0.8 under sudden traffic fluctuations, and a value of 0.8 to 1 under long-term large traffic fluctuations.

[0089] Specifically, the error covariance used to assess the credibility of the predicted value is used to adjust whether to lean more towards the predicted value or the observed value. If the observed value is very close to the predicted value, the system will become more confident and the trust in the prediction model will increase in the future (the error covariance will decrease). If the difference between the observed value and the predicted value is large, the system will become less confident and rely more on the observed value for correction.

[0090] It should be noted that the Kalman gain dynamically balances the weights of the predicted value and the observed value, and dynamically evaluates the credibility of the predicted value in combination with the error covariance. The error covariance is adjusted as the observed value and the predicted value are close. If the consistency between the two is high, the system increases the trust in the predicted value and reduces the correction range. If the gap is large, it relies more on the observed value to correct the prediction. The covariance matrix of process noise and measurement noise adapts to different network environments through the weight dynamic adjustment factor, ensuring that the system can maintain high prediction efficiency under stable conditions and respond quickly under fluctuating conditions, improving communication stability and the accuracy of delay compensation.

[0091] S604: Correct the predicted value of the communication channel delay according to the corrected Kalman gain and error covariance:

[0092]

[0093] in, represents the predicted value at time k+1, i.e. the predicted value of the corrected communication channel delay, express k Observation value at time The observation matrix of the mapping relationship between and the true value, represents the predicted value at time k+1 based on the state at time k, represents the Kalman gain at time k, that is, the corrected Kalman gain, represents the observation value at time k.

[0094] S605: Adjust the bandwidth adjustment amount according to the corrected prediction value to compensate for the communication channel delay.

[0095] Specifically, the compensation module constructs a smooth constraint function of the delay prediction value through the variational method to ensure that the prediction path is more stable, minimizes the constraint function value with the Kalman filter algorithm, and dynamically balances the weights of the prediction value and the observation value. The network noise correction mechanism is introduced to adjust the Kalman gain and error covariance in combination with process noise and measurement noise, optimize the delay prediction value according to the modified Kalman gain, and dynamically adjust the bandwidth allocation based on the optimized prediction results to achieve accurate compensation for communication channel delays and ensure network stability and resource utilization efficiency.

[0096] It should be noted that if there are multiple immediate medical requests at the same time, that is, multiple communication channels, the bandwidth adjustment amount can be balanced and adjusted in the following manner:

[0097]

[0098] in, represents the delay prediction value of the i-th communication channel at time k, represents the occupied bandwidth of the i-th communication channel, Represents the total available bandwidth, N represents the total number of current communication channels, State the constraints.

[0099] It is understandable that in the case of multiple concurrent instant medical requests, a dynamic balanced bandwidth allocation mechanism is used to adjust the bandwidth occupancy ratio according to the delay prediction value of each communication channel, while meeting the total available bandwidth limit, to avoid a certain channel occupying too many resources and affecting other channels, thereby ensuring the stability and fairness of all communication tasks and improving the overall resource utilization efficiency and service quality of the system.

[0100] Return module 7 is used to call the second acquisition module until the communication task is terminated.

[0101] In a possible implementation, the system further includes:

[0102] The warning module 8 is used to issue a warning when the duration of the communication channel delay is greater than a preset duration.

[0103] It should be noted that the early warning module monitors the delay status of the communication channel in real time. When the delay duration exceeds the preset threshold, the system immediately triggers an early warning. This design can help identify network anomalies or performance problems in a timely manner, remind relevant personnel to take emergency measures, prevent data transmission failures or medical service interruptions due to long delays, and further ensure the stability and service quality of smart medical care.

[0104] In actual application, the first terminal and the second terminal are connected through the dispatch center to realize flexible patient data sharing, remote diagnosis and treatment, and doctor collaboration. The dynamic encryption method combined with information entropy improves the security of data transmission, and the real-time and integrity of immediate medical tasks are ensured through the communication module. The communication channel delay is obtained in real time, and the variational algorithm and Kalman filter algorithm are combined for smooth compensation to avoid resource waste or connection interruption caused by drastic fluctuations in delay, thereby improving the stability, reliability and service efficiency of medical communications and meeting the needs of telemedicine in multiple scenarios.

[0105] Compared with the prior art, the present invention has at least the following beneficial technical effects:

[0106] In the present invention, the data of remote communication is encrypted by a sharding method combined with the information entropy of the data to be transmitted. The key can be dynamically generated according to the complexity of the data, the randomness and security of the encryption can be improved, and the single point leakage can be prevented by sharding storage, the anti-attack capability can be enhanced, and the integrity and privacy of data transmission can be ensured. During the communication process, the communication channel delay is collected in real time, the variational algorithm is combined, the communication channel delay is smoothly compensated by the Kalman filter algorithm, the communication channel delay is compensated by the variational algorithm and the Kalman filter algorithm, and the target path of the delay compensation is optimized by the variational algorithm to ensure that the delay changes are smooth and optimal; Kalman filtering fuses the observed value and the predicted value on this basis to improve the dynamic adaptability and accuracy of the delay change. The combination of the two can reduce the violent fluctuations and maintain the real-time and stability of the compensation. Avoid the frequent compensation strategy caused by delay fluctuations, so that even if the network delay fluctuates, the system is adjusted very gently, and other communication channels will not be disconnected due to the sudden allocation of excessive bandwidth in the same communication channel, saving network resources while ensuring the communication process, and maintaining the sustainability and stability of the entire smart medical process. Improving the data security of medical communications and the fluency of instant communications, reducing communication delays, and making medical communications with extremely high real-time requirements suitable for complex and changeable network scenarios.

[0107] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0108] The above embodiments only express several implementation methods of the present invention, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for those of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present invention, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the attached claims.

Claims

1. A smart medical system based on 5G remote communication, characterized in that: The smart medical system includes a first terminal, a dispatch center, and a second terminal connected in sequence; the system also includes: A first acquisition module, configured to acquire an instant medical treatment request initiated by the first terminal to the second terminal; an establishing module, configured to establish a communication channel between the first terminal and the second terminal through the dispatch center in response to the instant medical treatment request; An encryption module, used to dynamically encrypt the communication channel in a fragmented manner in combination with the information entropy of the data to be transmitted in the communication channel; A communication module, used for performing a communication task regarding the instant medical treatment request through an encrypted communication channel; A second acquisition module is used to acquire the communication channel delay in the process of performing the communication task; A compensation module, used to smoothly compensate the communication channel delay by combining a variational algorithm and a Kalman filter algorithm; The return module is used to call the second acquisition module until the communication task is terminated.

2. The smart medical system based on 5G remote communication according to claim 1 is characterized in that: The instant medical requests include video requests, voice requests, and requests for access to patient vital sign monitoring data.

3. The smart medical system based on 5G remote communication according to claim 2 is characterized in that: The establishment module is specifically used for: When the instant medical treatment request is a video request or a voice request, establishing the communication channel through the WebRTC protocol or the RTP protocol; In the case where the instant medical treatment request is a request for access to patient vital sign monitoring data, the communication channel is established through the MQTT protocol or the HTTP / 2 protocol.

4. The smart medical system based on 5G remote communication according to claim 1 is characterized in that: The encryption module is specifically used for: Acquire the data to be transmitted; Calculate the information entropy value of the data to be transmitted: ； in, represents the information entropy value of the data D to be transmitted, represents the probability of occurrence of the i-th symbol in the data to be transmitted, , n represents the total number of symbol types in the data to be transmitted, and log represents the logarithmic function; Generate a dynamic key based on the information entropy value: ； in, represents the random seed, Represents a dynamic key, Represents the exclusive OR operation; The dynamic key is sharded and distributedly stored in a distributed hash table: ； in, represents the jth dynamic key shard, represents the jth sharding parameter, N represents the upper limit of the sharding parameter, express The hash value of express Distributed hash table node, mod represents the modulus operation; The data to be transmitted is fragmented based on the number of fragments of the dynamic key: ； in, represents the i-th data fragment to be transmitted, , m represents the total number of data fragments to be transmitted, Indicates the total length of data to be transmitted. express Total length; The dynamic key obtained by sharding is used to encrypt the data to be transmitted obtained by each shard: ； in, Indicates based on The encryption algorithm, Express The i-th data fragment obtained after encryption; The data to be transmitted is transmitted in an encrypted manner to complete the encryption of the communication channel.

5. The smart medical system based on 5G remote communication according to claim 4 is characterized in that: The encryption module is also used for: When a receiving end receives the encrypted data to be transmitted, reorganizing each dynamic key fragment to obtain a dynamic key, wherein the receiving end includes the first terminal and the second terminal; The encrypted data to be transmitted is decrypted using the reorganized dynamic key, and the data to be transmitted is restored at the receiving end.

6. The smart medical system based on 5G remote communication according to claim 1, characterized in that: The compensation module is specifically used for: S601: Determine a constraint function of a predicted value of the communication channel delay by a variational method; S602: With the goal of minimizing the constraint function value, determine the predicted value of the communication channel delay through a Kalman filter algorithm; S603: Introducing network noise to correct the Kalman gain for balancing the predicted value weight and the observed value weight and the error covariance for evaluating the credibility of the predicted value, wherein the network noise includes process noise introduced due to the inability to describe the real noise and measurement noise caused by noise equipment errors; S604: Correct the predicted value of the communication channel delay according to the corrected Kalman gain and error covariance: ； in, represents the predicted value at time k+1, i.e. the predicted value of the corrected communication channel delay, express k Observation value at time The observation matrix of the mapping relationship between and the true value, represents the predicted value at time k+1 based on the state at time k, represents the Kalman gain at time k, that is, the corrected Kalman gain, represents the observation value at time k; S605: Adjust the bandwidth adjustment amount according to the corrected prediction value to compensate for the communication channel delay.

7. The smart medical system based on 5G remote communication according to claim 6 is characterized in that: The constraint function is specifically: ； in, represents the constraint function value, represents the predicted value at time k, express The true value of represents the initial time of integration, Indicates the end time of integration, represents the regularization parameter that controls the importance of smoothness, represents partial derivative, Represents the Lagrangian of the constraint function.

8. The smart medical system based on 5G remote communication according to claim 7 is characterized in that: The calculation method of the predicted value of the communication channel delay is specifically as follows: ； Among them, min means taking the minimum value, represents the predicted value at time k+1 based on the state at time k, and A represents the state transition matrix of how the current state affects the state at the next moment. Representation and The relevant k-time control input is the bandwidth adjustment amount, and B represents the control input A matrix of control inputs that affect the strength of the predicted values.

9. The smart medical system based on 5G remote communication according to claim 8, characterized in that: The Kalman gain for balancing the predicted value weight and the observed value weight and the error covariance for evaluating the credibility of the predicted value are specifically: ； in, represents the Kalman gain at time k, that is, the corrected Kalman gain, Represents the observed value at time k The observation matrix of the mapping relationship between the real value and the real value, the subscript T indicates the transpose, and Q indicates the process noise including the k-time process The process noise covariance matrix, R represents the measurement noise at time k The measurement noise covariance matrix is represents the stability factor, represents the error covariance with time k The associated forecast error covariance represents the uncertainty of the forecast value at time k+1, represents the corrected error covariance, I represents the unit matrix, represents the initial process noise, represents the initial measurement noise, represents the dynamic adjustment factor of the weight at time k, represents the process noise at time k, represents the measurement noise, The weight factor that controls the impact of noise.

10. The smart medical system based on 5G remote communication according to claim 1, characterized in that: Also includes: The early warning module is used to issue an early warning when the delay duration of the communication channel is greater than a preset delay duration.

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