Hospital area wireless network system supporting Internet of Things protocol

Abstract

The invention relates to the technical field of wireless communication, in particular to a hospital district wireless network system supporting an internet of things protocol, which is characterized in that a multi-protocol dynamic scheduling engine intelligently selects a protocol stack by utilizing a lightweight reinforcement learning model through a three-layer dynamic decision-making architecture; the time-sensitive hybrid topology network adopts dual-mode topology fusion to provide a star network and low-delay time slot distribution for emergency equipment; the cross-protocol security aggregation gateway realizes cross-protocol data encapsulation and field-level mapping based on a unified data head of the medical Internet of Things; the intelligent spectrum anti-interference unit monitors and analyzes a hospital frequency band electromagnetic environment in real time to obtain a spectrum sensing information result, and feeds back the spectrum sensing information result to the multi-protocol dynamic scheduling engine and time-sensitive hybrid topology network. According to the invention, the multi-protocol dynamic scheduling engine is combined with the time-sensitive hybrid topology network, so that the communication problems of equipment types and complex environments can be solved.

Description

Technical Field

[0001] The present invention relates to the field of wireless communication technology, and in particular to a hospital campus wireless network system supporting an Internet of Things protocol. Background Art

[0002] The hospital campus wireless network system that supports IoT protocols is designed to optimize multi-protocol compatibility and improve network resource allocation efficiency. Through a lightweight reinforcement learning model combined with a multi-protocol dynamic scheduling engine, it controls the protocol stack selection and network topology of medical equipment, realizes real-time and intelligent scheduling of network resources for medical equipment, ensures priority transmission of emergency medical equipment, and guarantees the stability and reliability of non-emergency equipment.

[0003] The existing hospital campus wireless network system often experiences network congestion during emergency medical data transmission. In addition, due to the wide variety of hospital equipment, complex network environment, and unstable channel quality, medical equipment may not be able to connect in time under high load, transmission delays may increase, and even data packet loss may occur. Therefore, a hospital campus wireless network system that supports the Internet of Things protocol is designed. Summary of the Invention

[0004] The purpose of the present invention is to provide a hospital campus wireless network system that supports the Internet of Things protocol to solve the problems raised in the above background technology that due to the wide variety of hospital equipment, complex network environment, and unstable channel quality, medical equipment may not be able to connect in time under high load, transmission delays may increase, and even data packet loss may occur.

[0005] A hospital campus wireless network system supporting the Internet of Things protocol, comprising: A multi-protocol dynamic scheduling engine that uses a three-layer dynamic decision-making architecture to intelligently select protocol stacks through a lightweight reinforcement learning model; A time-sensitive hybrid topology network that uses dual-mode topology fusion to establish a star network and allocate low-latency time slots for emergency medical equipment, and a self-organizing mesh network for non-emergency equipment and dynamically optimize multi-hop transmission paths; A cross-protocol security aggregation gateway that integrates the unified data header protocol for the medical Internet of Things, implements field-level mapping and encapsulation of multi-protocol data, and performs dynamic token authentication based on the national secret SM9 algorithm and zero-trust architecture; An intelligent spectrum anti-interference unit monitors the hospital frequency band in real time through software-defined radio, generates spectrum sensing results and feeds them back to the multi-protocol dynamic scheduling engine and the time-sensitive hybrid topology network to adjust the network configuration.

[0006] As a further improvement of the present technical solution, the multi-protocol dynamic scheduling engine implements multi-protocol dynamic scheduling based on hierarchical decision making, including the multi-protocol dynamic scheduling engine comprising: The device layer identifies the device type through embedded protocol tags and allocates the initial protocol stack; The network layer dynamically switches protocol stacks based on real-time channel quality assessment, where channel quality assessment uses a lightweight reinforcement learning model; At the business layer, the protocol stack is bound to the quality of service (QoS) based on the priority of medical data.

[0007] As a further improvement of the present technical solution, the method for dynamically switching the protocol stack of the network layer includes: Construct a network quality evaluation function to calculate the channel quality of the initial protocol stack based on signal strength, packet loss rate, and delay; When the channel quality is lower than the threshold, the network state space and action space are constructed, and the optimal protocol stack is selected through the reinforcement learning model; The reward function is combined with signal strength, packet loss rate, delay and protocol stack characteristics. Based on the Q value of the protocol stack in the network state space, the Q value is updated and the protocol stack decision is iteratively optimized.

[0008] As a further improvement of this technical solution, the business layer is configured as follows: Assign a low-latency, high-reliability protocol stack to emergency equipment data streams; Distribute low-bandwidth, low-power protocol stacks to common monitoring device data streams.

[0009] As a further improvement of the present technical solution, the time-sensitive hybrid topology network includes: Star-shaped time slot preemption module calculates preemptive time slot allocation based on the dynamic weight of devices; The Mesh path optimization module calculates the optimal multi-hop transmission path based on link quality index and energy surplus.

[0010] As a further improvement of this technical solution, the star-shaped time slot preemption module executes: Calculate dynamic weights based on device priority tags, signal-to-noise ratio, and signal strength; The theoretical time slot length is allocated based on the weight ratio, and time slot preemption is performed for high-priority devices.

[0011] As a further improvement of this technical solution, the Mesh path optimization module performs: Calculate the link quality index between devices, integrating signal strength, bit error rate and hop count parameters; The optimal multi-hop transmission path is selected based on the principle of minimizing the comprehensive path cost.

[0012] As a further improvement of this technical solution, the cross-protocol security aggregation gateway includes: The protocol data mapping module realizes multi-protocol data field mapping and time window aggregation through a unified data header structure; A dynamic trust chain authentication module that generates short-lived access tokens based on elliptic curve cryptography and performs context-aware verification.

[0013] As a further improvement of the present technical solution, the unified data header structure includes a device identifier, a data type code, a timestamp, and a checksum field, and maps the MQTT Topic and Zigbee Cluster ID to a standard code through protocol parsing rules.

[0014] As a further improvement of this technical solution, the intelligent spectrum anti-interference unit includes: Spectrum anti-interference module, used to scan the electromagnetic environment in real time and generate frequency band interference maps; The perception information feedback module is used to trigger protocol stack switching and network topology reconstruction based on the interference map.

[0015] Compared with the prior art, the present invention has the following beneficial effects: 1. Based on a multi-protocol collaborative scheduling algorithm and a network resource optimization strategy, the present invention can intelligently select the appropriate protocol stack and dynamically adjust the allocation of network resources according to the real-time status of the device, data transmission requirements, and network load, thereby improving the connection efficiency of the device, reducing latency, ensuring the transmission channel of high-priority medical equipment, and optimizing the bandwidth usage of low-priority equipment.

[0016] 2. The present invention introduces a dynamic network topology adjustment mechanism based on deep reinforcement learning to achieve real-time monitoring of device distribution, network quality and channel status, automatically optimize the network topology structure, ensure that medical equipment can be quickly and stably connected to the network in different areas and under different loads, avoid network congestion, and ensure efficient and stable data transmission. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 It is the overall flow chart of the present invention; The meaning of each number in the figure is: 1. Multi-protocol dynamic scheduling engine; 11. Device layer; 12. Network layer; 13. Business layer; 2. Time-sensitive hybrid topology network; 21. Star-shaped time slot preemption module; 22. Mesh path optimization module; 3. Cross-protocol security aggregation gateway; 31. Protocol data mapping module; 32. Dynamic trust chain authentication module; 4. Intelligent spectrum anti-interference unit; 41. Spectrum anti-interference module; 42. Perception information feedback module. DETAILED DESCRIPTION

[0018] The following will provide a clear and complete description of the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0019] See also Figure 1 As shown, a hospital campus wireless network system supporting the Internet of Things protocol is provided, including: A multi-protocol dynamic scheduling engine 1, which uses a three-layer dynamic decision-making architecture and a lightweight reinforcement learning model to intelligently select a protocol stack; The multi-protocol dynamic scheduling engine 1 implements multi-protocol dynamic scheduling based on hierarchical decision making, including a device layer 11, a network layer 12, and a service layer 13; The device layer 11 automatically identifies the device type through embedded protocol tags and assigns an initial protocol stack to the device based on the device type. Life monitors are automatically tagged with the MQTT+Urgent protocol to ensure low latency and reliable transmission. Environmental sensors are tagged with the CoAP+LowPower protocol to accommodate low power consumption and low bandwidth requirements. The network layer 12 uses a lightweight reinforcement learning model to evaluate channel quality in real time and dynamically switch device protocol stacks; Reinforcement learning is a machine learning technology used in a variety of fields, including network optimization, autonomous driving, and robotic control. The lightweight reinforcement learning model, optimized for resource-constrained devices, is a simplified and optimized form of the reinforcement learning model and is suitable for edge computing and IoT scenarios within this hospital's wireless network. The service layer 13 allocates a transmission strategy according to the priority of the medical data and strongly binds the device protocol stack with QoS.

[0020] In this embodiment, the network layer 12 uses a lightweight reinforcement learning model to evaluate channel quality in real time and dynamically switch device protocol stacks. The specific method steps are as follows: S1.2.1. Based on signal strength, packet loss rate, and delay, a network quality evaluation function is constructed to calculate the initial protocol stack channel quality: Normalize each indicator: Map the signal strength to the interval [0,1]: in, (worst signal), (Best signal).

[0021] Reverse mapping of packet loss rate: Delayed reverse mapping: in, (maximum acceptable delay).

[0022] Network quality assessment function: ; in, is the initial protocol stack channel quality; is the signal strength weight coefficient; is the signal strength; is the packet loss rate weight coefficient; is the packet loss rate; is the delay weight coefficient; For delay, 、 、 are the normalized values of signal strength, packet loss rate and delay respectively. In this embodiment, .

[0023] In this embodiment, before dynamic protocol switching, it is necessary to first determine whether the protocol stack needs to be switched by evaluating the network channel quality in real time; the network quality evaluation includes signal strength, packet loss rate, and latency; S1.2.2. If the initial protocol stack channel quality is lower than the device requirement, dynamically switch the device protocol stack and execute S1.2.3. If the initial protocol stack channel quality meets the device requirement, perform real-time continuous monitoring. S1.2.3. Construct the network state space at time t based on signal strength, packet loss rate, and delay: ; in, For the current moment; is the network state space at time t; is the signal strength at time t; is the packet loss rate at time t; is the delay at time t; S1.2.4. Construct the network action space at time t based on the network state space at time t and the initial protocol stack: ; in, The protocol stack selected at time t; is the Q value of the protocol stack in the network state space at time t, which represents the expected benefit of the protocol stack in the current network environment. It is a set of protocol stacks available for the current device.

[0024] S1.2.5. Constructing the reward function: ; in, is the reward function at time t; Protocol Stack The natural impact on rewards is automatically generated through its throughput, power consumption and other characteristics; specifically: in, Protocol Stack The measured throughput (unit: Mbps) Protocol Stack The transmission success rate is in the range [0,1]), Protocol Stack The power consumption (unit: mW). , , Indicates the maximum value of all protocol stacks in the system (used for normalization). , represents the throughput weight, , represents the reliability weight, , represents the power consumption weight.

[0025] S1.2.6. Based on the network state space at time t, the selected protocol stack, and the reward function, the lightweight reinforcement learning model uses the Q-learning strategy to update the Q value of the initial protocol stack in the network state space at time t: in, is the learning rate; is the discount factor; The protocol stack selected at time t+1; is the network state space at time t+1; It is the maximum Q value estimate of all protocol stacks in the network state space at time t+1; To execute the protocol stack at time t The maximum Q value brought about by this embodiment is , 0.9≤ ≤0.99.

[0026] S1.2.7. Based on the Q value of the protocol stack in the network state space at time t, the lightweight reinforcement learning model selects the protocol stack with the largest Q value according to the network state space at time t .

[0027] In this embodiment, the service layer 13 allocates a transmission strategy based on the priority of medical data and strongly binds the device protocol stack with QoS. The specific steps are as follows: S1.3.1. Based on the priority of medical data, assign the low-latency and high-reliability protocol stack to high-priority medical data; S1.3.2. Based on the priority of medical data, assign the low-bandwidth and low-power protocol stack to low-priority medical data.

[0028] High-priority medical data includes emergency instructions, real-time vital signs data, and emergency medical reports; low-priority medical data includes log uploads, daily health data, and non-emergency examination reports; It also includes a time-sensitive hybrid topology network 2, which uses dual-mode topology fusion to provide a star network and low-latency time slot allocation for emergency devices, while non-emergency devices use a mesh relay network for multi-hop transmission and dynamic avoidance of signal interference; The time-sensitive hybrid topology network 2 includes a star-shaped time slot preemption module 21 and a Mesh path optimization module 22; The star-shaped time slot preemption module 21 is used to build a low-latency star network for emergency equipment; The Mesh path optimization module 22 builds a self-organizing Mesh network for non-emergency devices, dynamically optimizes multi-hop transmission paths and avoids signal interference.

[0029] In this embodiment, the star-shaped time slot preemption module 21 is used to build a low-latency star-shaped network for emergency equipment. The specific method and steps are as follows: S2.1.1. Dynamic registration of device priority: The device sends a registration request with a priority tag to the central controller, which then calculates the dynamic weight of the device: ; in, is the device index in the dynamic weight; is the dynamic weight of the k-th device; is the priority label weight; is the priority label of the kth device, It is the last level priority label; is the signal-to-noise ratio weight; is the signal-to-noise ratio of the kth device; is the maximum signal-to-noise ratio; is the signal strength weight; is the signal strength of the kth device; is the signal strength threshold.

[0030] In this embodiment, priority labels are divided into five levels, from level one to level five; The priority label representing k devices is level one; The priority label for k devices is level 2, and so on; the signal strength threshold is -70dBm.

[0031] S2.1.2. Calculate the theoretical time slot length of the kth device based on the dynamic weight of the kth device and the time slot period: ; in, is the theoretical time slot length of the kth device; is the time slot period; is the first device index in the time slot length; is the total number of registered devices; For the Dynamic weight of each device; If you newly register a device Priority , then the time slot is forced to be allocated, realizing the preemptive TDMA time slot allocation function: ; in, For the The actual time slot length of each device; For the The theoretical time slot length of each device; A collection of devices with a priority label lower than level 4; is the second device index in the time slot length; For the The theoretical time slot length of a device.

[0032] In this embodiment, the Mesh path optimization module 22 builds a self-organizing Mesh network for non-emergency devices, dynamically optimizes multi-hop transmission paths and avoids signal interference. The specific method steps are as follows: S2.2.1 Calculation ; in, is the index of the first device in the transmission path; An index for a second device in the transmission path; is the link quality index between device m and device n, ; is the signal strength between device m and device n; Maximum signal strength, Minimum signal strength; is the bit error rate attenuation coefficient, 2 ≤ ≤ 5; is the bit error rate between device m and device n; is the number of hops between device m and device n; is the hop penalty factor, 1≤ ≤2.

[0033] S2.2.2. Based on the link quality index between devices, the optimal multi-hop transmission path for non-emergency devices is calculated to achieve the transmission path optimization function for non-emergency devices: in, is the path index; is the a-th path; For path comprehensive costs; is the energy weight coefficient; the energy weight coefficient represents the proportion of the device energy in the path; For equipment Energy surplus, For full energy; ; in, is the optimal multi-hop transmission path; is the set of all paths; is the path index; For the path.

[0034] It also includes a cross-protocol security aggregation gateway 3, which implements cross-protocol data encapsulation and field-level mapping based on the unified data header of the medical Internet of Things. The gateway integrates the national secret SM9 algorithm and zero-trust architecture, dynamically allocates access tokens and performs real-time verification. The cross-protocol security aggregation gateway 3 includes a protocol data mapping module 31 and a dynamic trust chain authentication module 32; In this embodiment, the protocol data mapping module 31 implements multi-protocol data field-level mapping and format normalization through the unified data header of the medical Internet of Things. The specific method steps are as follows: S3.1.1. Define a unified data header structure: ; in, To unify the data header structure; Unique identifier for the device; Encode the data type; is the timestamp; is a cyclic redundancy check code; S3.1.2. Perform regular expression parsing on the Topic field of the MQTT protocol, extract key information and encode it into the DataClass. Perform table lookup mapping on the Cluster ID of the Zigbee protocol to generate the standard DataClass code. S3.1.3. Aggregate data based on time windows: in, is the aggregated data set; is the time window index; is the total duration of data collection; is the time window size; For data index; For the pieces of data; For the The timestamp of the data item; For the The time interval of a time window.

[0035] In this embodiment, the dynamic trust chain authentication module 32 is based on the national secret SM9 algorithm and the zero trust architecture, and is used for dynamic token allocation and context-aware verification of devices. The specific method is as follows: S3.2.1. Using hash to elliptic curve group based on device identity, master private key, and generator Function Compute device public key, used to generate a short-term access token; S3.2.2. Verify the validity of the short-term access token and detect the matching between the verification device and the user terminal.

[0036] It also includes an intelligent spectrum anti-interference unit 4, which uses software-defined radio technology to monitor and analyze the hospital frequency band electromagnetic environment in real time to obtain spectrum sensing information results, and feeds back the spectrum sensing information results to the multi-protocol dynamic scheduling engine 1 and the time-sensitive hybrid topology network 2.

[0037] The intelligent spectrum anti-interference unit 4 includes an electromagnetic spectrum anti-interference module 41 and a perception information feedback module 42 as follows: In this embodiment, the spectrum anti-interference module 41 is used to collect electromagnetic spectrum data in real time, analyze and evaluate the electromagnetic spectrum data to obtain spectrum sensing information results, adjust frequency band resources and update the system's working frequency band configuration.

[0038] The perception information data results include signal strength, interference assessment, and frequency band selection.

[0039] In this embodiment, the sensing information feedback module 42 is used to feed back spectrum sensing information results to the multi-protocol dynamic scheduling engine 1 and the time-sensitive hybrid topology network 2 to re-perform dynamic protocol stack switching and network configuration.

[0040] The basic principles, main features, and advantages of the present invention are shown and described above. It should be understood by those skilled in the art that the present invention is not limited to the above-described embodiments. The above-described embodiments and descriptions are merely preferred examples of the present invention and are not intended to limit the present invention. Various changes and modifications may be made to the present invention without departing from the spirit and scope of the present invention, and such changes and modifications fall within the scope of the invention claimed.

Claims

1. A hospital campus wireless network system supporting the Internet of Things protocol, characterized in that: include: A multi-protocol dynamic scheduling engine (1), wherein the multi-protocol dynamic scheduling engine (1) adopts a three-layer dynamic decision-making architecture and intelligently selects a protocol stack through a lightweight reinforcement learning model; A time-sensitive hybrid topology network (2), wherein the time-sensitive hybrid topology network (2) uses dual-mode topology fusion to establish a star network for emergency medical equipment and allocate low-latency time slots, and to build a self-organizing mesh network for non-emergency equipment and dynamically optimize multi-hop transmission paths; A cross-protocol security aggregation gateway (3), wherein the cross-protocol security aggregation gateway (3) integrates a unified data header protocol of the medical Internet of Things, realizes field-level mapping and encapsulation of multi-protocol data, and performs dynamic token authentication based on the national secret SM9 algorithm and zero-trust architecture; An intelligent spectrum anti-interference unit (4) monitors the hospital frequency band in real time through software-defined radio, generates spectrum sensing results and feeds them back to the multi-protocol dynamic scheduling engine (1) and the time-sensitive hybrid topology network (2) to adjust the network configuration.

2. The hospital campus wireless network system supporting the Internet of Things protocol according to claim 1, characterized in that: The multi-protocol dynamic scheduling engine (1) comprises: Device layer (11), identifies the device type through embedded protocol tags and allocates the initial protocol stack; The network layer (12) dynamically switches the protocol stack based on real-time channel quality assessment, where the channel quality assessment uses a lightweight reinforcement learning model; The business layer (13) binds the protocol stack with the quality of service (QoS) according to the priority of medical data.

3. The hospital campus wireless network system supporting the Internet of Things protocol according to claim 2, characterized in that: The method for dynamically switching the protocol stack of the network layer (12) includes: Construct a network quality evaluation function to calculate the channel quality of the initial protocol stack based on signal strength, packet loss rate, and delay; When the channel quality is lower than the threshold, the network state space and action space are constructed, and the optimal protocol stack is selected through the reinforcement learning model; The reward function is combined with signal strength, packet loss rate, delay and protocol stack characteristics. Based on the Q value of the protocol stack in the network state space, the Q value is updated and the protocol stack decision is iteratively optimized.

4. The hospital campus wireless network system supporting the Internet of Things protocol according to claim 3, characterized in that: The business layer (13) is configured as follows: Assign a low-latency, high-reliability protocol stack to emergency equipment data streams; Distribute low-bandwidth, low-power protocol stacks to common monitoring device data streams.

5. The hospital campus wireless network system supporting the Internet of Things protocol according to claim 4 is characterized in that: The time-sensitive hybrid topology network (2) includes: A star-shaped time slot preemption module (21) calculates preemptive time slot allocation based on the dynamic weight of the device; The Mesh path optimization module (22) calculates the optimal multi-hop transmission path through the link quality index and energy surplus.

6. The hospital campus wireless network system supporting the Internet of Things protocol according to claim 5, characterized in that: The star-shaped time slot preemption module (21) executes: Calculate dynamic weights based on device priority tags, signal-to-noise ratio, and signal strength; The theoretical time slot length is allocated based on the weight ratio, and time slot preemption is performed for high-priority devices.

7. The hospital campus wireless network system supporting the Internet of Things protocol according to claim 5, characterized in that: The Mesh path optimization module (22) performs: Calculate the link quality index between devices, integrating signal strength, bit error rate and hop count parameters; The optimal multi-hop transmission path is selected based on the principle of minimizing the comprehensive path cost.

8. The hospital campus wireless network system supporting the Internet of Things protocol according to claim 1, characterized in that: The cross-protocol security aggregation gateway (3) includes: The protocol data mapping module (31) realizes multi-protocol data field mapping and time window aggregation through a unified data header structure; A dynamic trust chain authentication module (32) generates short-term access tokens based on elliptic curve cryptography and performs context-aware authentication.

9. The hospital campus wireless network system supporting the Internet of Things protocol according to claim 8, characterized in that: The unified data header structure includes a device identifier, a data type code, a timestamp, and a checksum field, and maps the MQTT Topic and Zigbee Cluster ID to a standard code through protocol parsing rules.

10. The hospital campus wireless network system supporting the Internet of Things protocol according to claim 9, characterized in that: The intelligent spectrum anti-interference unit (4) comprises: A spectrum anti-interference module (41) is used to scan the electromagnetic environment in real time and generate a frequency band interference spectrum; The perception information feedback module (42) is used to trigger protocol stack switching and network topology reconstruction according to the interference map.

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