Wireless gateway and wireless connection optimization method

Abstract

The embodiment of the invention discloses a wireless gateway and a wireless connection optimization method. The wireless gateway comprises a connection management module used for discovering and connecting a data acquisition terminal in a connectable state; the data acquisition module is used for acquiring the physiological parameters acquired by each data acquisition terminal from the connection management module; the risk assessment module is used for determining the health risk level of each patient based on the physiological parameters; the connection evaluation module is used for acquiring evaluation characteristic data of each data acquisition terminal from the data acquisition module and the risk evaluation module, and determining a connection priority and a connection parameter of each data acquisition terminal based on the evaluation characteristic data; and the connection management module is also used for preferentially configuring corresponding connection parameters for the data acquisition terminals with higher connection priorities based on the connection priorities of the data acquisition terminals.

Description

Technical Field

[0001] This application relates to the field of communications, specifically to a wireless gateway and a wireless connection optimization method. Background Technology

[0002] In the field of communications, wireless gateways serve as the unified communication interface for terminal devices, undertaking the task of establishing and maintaining wireless connections for these devices. With the rapid development of IoT technology, various terminal devices, such as data acquisition terminals (e.g., sensors, wearable devices, industrial monitoring equipment, smart home devices), are being deployed in large numbers. Data acquisition terminals typically connect to wireless gateways via wireless communication technologies such as Bluetooth, Wi-Fi, and ZigBee. The wireless gateway then aggregates the data collected by each data acquisition terminal and uploads it to cloud servers or business platforms via Ethernet, 4G / 5G, or other backhaul methods.

[0003] Currently, in various scenarios such as smart homes, industrial IoT, smart offices, environmental monitoring, and smart medical monitoring, the deployment mode of "one wireless gateway for multiple terminals" and "one area for multiple terminals" is common. Taking the smart medical monitoring scenario as an example, a wireless gateway may need to connect to multiple data acquisition terminals such as blood pressure monitors, electrocardiogram monitors, pulse oximeters, temperature patches, and infusion pumps for different patients simultaneously. This requires the wireless gateway to have the ability to access multiple data acquisition terminals concurrently and an efficient resource scheduling mechanism.

[0004] In related technologies, wireless gateways typically employ a fixed connection strategy, such as configuring the same fixed connection parameters for multiple connected data acquisition terminals. However, in a "one wireless gateway, multiple terminals" deployment model, users have different needs for the data collected by different data acquisition terminals. For example, users (doctors or hospital management platforms, etc.) have a higher requirement for the real-time physiological parameters of critically ill patients, so the connection parameters configured for different data acquisition terminals should also be different. Consequently, wireless gateways using a fixed connection strategy struggle to meet the differentiated connection needs of different data acquisition terminals. Summary of the Invention

[0005] This application provides a wireless gateway and a wireless connection optimization method, which is used to determine the health risk level of each patient based on the physiological parameters collected by the data acquisition terminal corresponding to each patient, and to determine the connection priority and connection parameters of each data acquisition terminal by combining the dimensional identifier of the physiological parameters collected by the data acquisition terminal of each patient, thereby meeting the differentiated connection needs of different data acquisition terminals.

[0006] In a first aspect, embodiments of this application provide a wireless gateway, which includes:

[0007] The connection management module is used to discover and connect to data acquisition terminals that are in a connectable state; among them, there are multiple data acquisition terminals for the same patient, and the multiple data acquisition terminals collect different dimensions of the patient's physiological parameters respectively; The data acquisition module is used to obtain physiological parameters collected by each data acquisition terminal from the connection management module; the physiological parameters carry patient identifiers and dimension identifiers. The risk assessment module is used to obtain physiological parameters collected by each data acquisition terminal from the data acquisition module, and to determine the health risk level of each patient based on the physiological parameters. The connection assessment module is used to obtain assessment feature data of each data acquisition terminal from the risk assessment module, and determine the connection priority and connection parameters of each data acquisition terminal based on the assessment feature data; wherein, the assessment feature data includes at least the dimension identifier of the physiological parameters collected by the corresponding data acquisition terminal, and the health risk level of the patient to which the corresponding data acquisition terminal belongs. The connection management module is also used to obtain the connection priority and connection parameters of each data acquisition terminal from the data acquisition module and the connection evaluation module, and to configure the corresponding connection parameters for the data acquisition terminal with higher connection priority based on the connection priority of each data acquisition terminal.

[0008] In some embodiments, the risk assessment module is specifically used for: Obtain physiological parameters collected by each data acquisition terminal from the data acquisition module; Based on the patient identifier carried by the physiological parameters, the corresponding physiological parameters of each patient are determined. Based on the physiological parameters corresponding to each patient, a health risk score is determined for each patient; Based on each patient's health risk score, the health risk level of each patient is determined.

[0009] In some embodiments, the signal output terminal of the risk assessment module is also connected to the signal input terminal of the connection management module; The risk assessment module is also used for: In response to the fact that the health risk score of the first patient is greater than the risk score threshold, the risk data acquisition terminal corresponding to the physiological parameters that are at risk is determined based on the physiological parameters collected by each data acquisition terminal corresponding to the first patient. Correspondingly, the connection management module is also used for: Shorten the link connection interval in the connection parameters of the risk data collection terminal.

[0010] In some embodiments, the wireless gateway further includes a local alarm module, the signal input terminal of which is connected to the signal output terminal of the risk assessment module; The local alarm module is used to issue visual and / or auditory alarms in response to the first patient's health risk score being greater than the risk score threshold; the visual and / or auditory alarms carry physiological parameters indicating the risk.

[0011] In some embodiments, the data acquisition module further includes a protocol adaptive parsing submodule. The signal input terminal of the protocol adaptive parsing submodule serves as the signal input terminal of the data acquisition module and is connected to the signal output terminal of the connection management module. The signal output terminal of the protocol adaptive parsing submodule serves as the signal output terminal of the data acquisition module and is connected to the signal input terminal of the risk assessment module. The protocol adaptive parsing submodule is used for: The system obtains the raw physiological parameters collected by each data acquisition terminal from the connection management module, and queries the local template library to see if there is a first parsing template corresponding to the device model based on the device model carried in the raw physiological parameters. If it exists, the original physiological parameters are parsed using the first parsing template to obtain the physiological parameters collected by each data acquisition terminal; If it does not exist, the original physiological parameters are analyzed for protocol features to generate a second parsing template corresponding to the device model, and the second parsing template is stored in the local template library; the original physiological parameters are parsed using the second parsing template to obtain the physiological parameters collected by each data acquisition terminal.

[0012] In some embodiments, when a first parsing template corresponding to the device model does not exist in the local template library, the protocol adaptive parsing submodule is specifically used for: The raw physiological parameters within a preset time window are obtained, and the protocol features of the raw physiological parameters are analyzed to obtain the initial parsing template of the device model of the data acquisition terminal that collects the raw physiological parameters. The protocol features include at least the length distribution features of the data frame, the length pattern features of each field, the distribution features of the bits, the periodic structure features of the data frame, and the features of the check field. In response to the calibration operation, the initial parsing template is calibrated to obtain the second parsing template corresponding to the device model.

[0013] In some embodiments, the wireless gateway further includes a data quality assessment module, the signal input terminal of which is connected to the signal output terminal of the data acquisition module, and the signal output terminal of the data quality assessment module is connected to the signal input terminal of the connection management module and / or the alarm module. The data quality assessment module is used for: Obtain physiological parameters collected by each data acquisition terminal from the data acquisition module; Based on the physiological parameters collected by each data acquisition terminal, output the data quality score of each physiological parameter; In response to a data quality score for a first physiological parameter falling below a data quality score threshold, the type of data anomaly event that caused the data quality score for the first physiological parameter to fall below the data quality score threshold is determined. If the data anomaly event type is a link event, a connection parameter adjustment suggestion for the first data acquisition terminal is sent to the connection management module based on the link event; or / and, if the data anomaly event type is a non-link event, an alarm message for the non-link event is sent to the alarm module; wherein, the first data acquisition terminal refers to the data acquisition terminal that collects the first physiological parameter; the connection parameter adjustment suggestion is used to trigger the connection management module to adjust the connection parameters associated with the first data acquisition terminal and the link event, so as to improve the link quality between the first data acquisition terminal and the wireless gateway; the alarm message is used to prompt that the first physiological parameter needs to be re-collected or to prompt manual intervention.

[0014] In some embodiments, the wireless gateway further includes a remote interaction module; the signal input terminal of the remote interaction module is connected to the signal output terminal of the data acquisition module and the risk assessment module, and the signal output terminal of the remote interaction module is connected to the signal input terminal of the risk assessment module and the connection assessment module. The remote interaction module is used to upload physiological parameters collected by each data acquisition terminal and the health risk level of each patient to the business server. The remote interaction module is also used to: send updated information from the business server to the risk assessment module and / or the connection assessment module; wherein the updated information is used to update the first assessment strategy of the risk assessment module and / or update the second assessment strategy of the connection assessment module.

[0015] In some embodiments, the wireless gateway further includes a human-machine interaction module; the signal input terminal of the human-machine interaction module is connected to the signal output terminal of the data acquisition module and the risk assessment module. The human-computer interaction module is used to display the physiological parameters collected by each data acquisition terminal and the health risk level of each patient on the business display device.

[0016] Secondly, embodiments of this application provide a wireless connection optimization method, the method comprising: Discover and connect to data acquisition terminals that are in a connectable state; among them, there are multiple data acquisition terminals for the same patient, and the multiple data acquisition terminals collect physiological parameters of the patient in different dimensions respectively; Acquire physiological parameters collected by each data acquisition terminal; the physiological parameters carry patient identifiers and dimension identifiers; Based on physiological parameters, the health risk level of each patient is determined; The assessment feature data of each data acquisition terminal is obtained, and the connection priority and connection parameters of each data acquisition terminal are determined based on the assessment feature data. The assessment feature data includes at least the dimension identifier of the physiological parameters collected by the corresponding data acquisition terminal and the health risk level of the patient to which the corresponding data acquisition terminal belongs. Based on the connection priority of each data acquisition terminal, the corresponding connection parameters are configured for the data acquisition terminals with higher connection priority.

[0017] The wireless gateway provided in this application embodiment discovers and connects to data acquisition terminals in a connectable state by setting a connection management module. It limits the number of data acquisition terminals corresponding to the same patient to collect different physiological parameters. The data acquisition module acquires physiological parameters carrying patient and dimension identifiers. The risk assessment module determines the health risk level of each patient based on the physiological parameters. The connection assessment module then acquires assessment feature data including at least the dimension identifier and the patient's health risk level, and determines the connection priority and connection parameters of each data acquisition terminal based on this assessment feature data. Finally, the connection management module configures corresponding connection parameters for data acquisition terminals with higher priority based on the connection priority. Thus, in a "one wireless gateway, multiple terminals" deployment mode, the connection priority and connection parameters of different data acquisition terminals can be dynamically adjusted according to the patient's health risk level and dimension identifier. This allows data acquisition terminals corresponding to patients with higher health risk levels, as well as data acquisition terminals corresponding to more important physiological parameter dimensions of the same patient, to obtain higher connection priority and better connection parameter configurations. This solves the technical problem in related technologies where fixed connection strategies are used, making it difficult to meet the differentiated connection needs of different data acquisition terminals. It achieves intelligent scheduling of wireless gateway resources and improves the real-time performance and reliability of key physiological parameter acquisition.

[0018] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and do not limit this application. Attached Figure Description

[0019] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are merely embodiments of this application. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort, and this application can be applied to other similar scenarios based on the provided drawings.

[0020] Figure 1 This is a schematic diagram of the structure of a wireless gateway provided in an embodiment of this application; Figure 2This is a flowchart illustrating a wireless connectivity optimization method provided in an embodiment of this application. Detailed Implementation

[0021] The present application will now be described in further detail with reference to the accompanying drawings and embodiments. It is to be understood that the specific embodiments described herein are merely illustrative of the invention and not intended to limit it. The described embodiments are only a part of the embodiments of this application, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0022] It should be noted that the terms "system," "device," "unit," and / or "module" used in this application are methods of distinguishing different components, elements, parts, sections, or assemblies at different levels. However, if other terms can achieve the same purpose, they can be replaced by other expressions.

[0023] Hereinafter, the terms "first," "second," and "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first," "second," or "third" may explicitly or implicitly include one or more of that feature.

[0024] In the field of communications, wireless gateways serve as the unified communication interface for terminal devices, undertaking the task of establishing and maintaining wireless connections for these devices. With the rapid development of IoT technology, various terminal devices, such as data acquisition terminals (e.g., sensors, wearable devices, industrial monitoring equipment, smart home devices, etc.), are being deployed in large numbers.

[0025] Data acquisition terminals typically connect to wireless gateways via wireless communication technologies such as Bluetooth, Wi-Fi, and ZigBee. Corresponding to the type of wireless communication technology, wireless gateways may also include Bluetooth gateways, Wi-Fi gateways, ZigBee gateways, etc.

[0026] In various scenarios such as smart homes, industrial IoT, smart offices, environmental monitoring, and smart medical monitoring, the deployment mode of "one wireless gateway for multiple terminals" and "one area for multiple terminals" is common.

[0027] Taking intelligent medical monitoring scenarios as an example, with the widespread adoption of wearable devices, bedside monitoring devices, and home health management devices, a large number of physiological parameters (such as ECG, blood pressure, blood oxygen saturation, body temperature, blood glucose, and respiratory rate) can be collected in real time through data acquisition terminals with corresponding functions. These data acquisition terminals often use Bluetooth Low Energy (BLE) mode to conduct short-range wireless communication with host devices (such as mobile phones, tablets, or Bluetooth gateways), and then the host devices upload the data to the hospital information system or cloud-based health management platform via Wi-Fi / Ethernet / 4G / 5G, etc. Specifically, in hospital wards, it is common to see "one person, multiple terminals" and "one bed, multiple terminals." For example, a patient may simultaneously connect to multiple data acquisition terminals such as a blood pressure monitor, ECG monitor, pulse oximeter, temperature patch, and infusion pump. In this case, a Bluetooth gateway (such as a tablet-shaped Bluetooth gateway) is needed to uniformly access, manage, and forward the multi-source physiological data collected by these data acquisition terminals.

[0028] In related technologies, wireless gateways typically employ a fixed connection strategy. Let's take an example where the wireless gateway is a Bluetooth gateway, and the terminal devices are multiple data acquisition terminals such as blood pressure monitors, ECG monitors, pulse oximeters, temperature patches, and infusion pumps. The Bluetooth gateway usually configures the same fixed connection parameters for blood pressure monitors, ECG monitors, pulse oximeters, temperature patches, and infusion pumps, such as the same scan interval and link connection interval. However, in actual medical monitoring scenarios, the requirements for the physiological parameters collected by different data acquisition terminals vary greatly. For example, the real-time requirements for physiological parameters of critically ill patients are higher than those for patients with mild symptoms, requiring the link connection interval of the data acquisition terminal for critically ill patients to be lower than that for patients with mild symptoms. Similarly, the real-time requirements for ECG monitoring data and pulse oximeter data are much greater than the real-time requirements for infusion pump data, requiring the link connection interval in the connection parameters of ECG monitors and pulse oximeters to be lower than that of infusion pumps.

[0029] However, Bluetooth gateways employing a fixed connection strategy struggle to meet the diverse connectivity needs of different data acquisition terminals. Similarly, in other scenarios such as smart homes, industrial IoT, smart offices, and environmental monitoring, wireless gateways using a fixed connection strategy also fail to meet these diverse connectivity requirements.

[0030] Based on this, in order to solve the problem that wireless gateways using fixed connection strategies cannot meet the differentiated connection requirements of different data acquisition terminals, this application provides a wireless gateway and a wireless connection optimization method. By determining the health risk level of each patient based on the physiological parameters collected by the data acquisition terminal corresponding to each patient, and combining the dimensional identifiers of the physiological parameters collected by the data acquisition terminal of each patient, the connection priority and connection parameters of each data acquisition terminal are determined, thereby meeting the differentiated connection requirements of different data acquisition terminals.

[0031] The wireless gateway provided in this application embodiment can be any type of wireless gateway, such as a Bluetooth gateway, Wi-Fi gateway, ZigBee gateway, etc. The wireless gateway can take any form, such as a tablet-shaped wireless gateway. It is understood that the wireless gateway should include basic hardware components, such as a power module, processor, memory, wireless communication module, and network communication module. The power module may include a power supply and a power management chip, and is used to supply power to the processor, memory, wireless communication module, and network communication module. The processor is used to establish and maintain the wireless connection of the data acquisition terminal according to the connection strategy. The memory is used to store necessary connection information, such as a connection parameter table, a list of connected data acquisition terminals, and parsing templates for different models of data acquisition terminals. The parsing templates are used to parse the raw physiological parameters collected by the data acquisition terminal into structured physiological parameters. The wireless communication module is used to establish a wireless connection with the data acquisition terminal under the control of the processor. The network communication module is used to upload the data collected by the data acquisition terminal to a cloud server or hospital management platform via Ethernet, 4G / 5G, or other backhaul methods.

[0032] Figure 1 This is a schematic diagram of the structure of a wireless gateway provided in an embodiment of this application.

[0033] like Figure 1 As shown, in addition to the basic hardware components described above, the wireless gateway provided in this application embodiment also includes a software component. The software component consists of multiple functional modules, which can run on the processor of the basic hardware and are used to provide the wireless gateway with connection strategies that meet the differentiated connection requirements of different data acquisition terminals.

[0034] Specifically, the software includes a connection management module 101, a data acquisition module 102, a risk assessment module 103, and a connection assessment module 104. The signal output terminal of the connection management module 101 is connected to the signal input terminal of the data acquisition module 102. The signal output terminal of the data acquisition module 102 is connected to the signal input terminal of the risk assessment module 103. The signal output terminal of the data acquisition module 102 is also connected to the signal input terminal of the connection assessment module 104. The signal output terminal of the risk assessment module 103 is connected to the signal input terminal of the connection assessment module 104. The signal output terminal of the connection assessment module 104 is connected to the signal input terminal of the connection management module 101.

[0035] The connection management module 101 is used to discover and connect to data acquisition terminals that are in a connectable state; multiple data acquisition terminals correspond to the same patient, and the multiple data acquisition terminals collect physiological parameters of the patient in different dimensions respectively. The data acquisition module 102 is used to obtain physiological parameters collected by each data acquisition terminal from the connection management module 101; the physiological parameters carry patient identifiers and dimension identifiers. The risk assessment module 103 is used to obtain physiological parameters collected by each data acquisition terminal from the data acquisition module 102, and determine the health risk level of each patient based on the physiological parameters. The connection assessment module 104 is used to obtain assessment feature data of each data acquisition terminal from the data acquisition module 102 and the risk assessment module 103, and determine the connection priority and connection parameters of each data acquisition terminal based on the assessment feature data. The assessment feature data includes at least the dimension identifier of the physiological parameters collected by the corresponding data acquisition terminal and the health risk level of the patient to which the corresponding data acquisition terminal belongs. Specifically, the connection assessment module 104 obtains the dimension identifier of the physiological parameters collected by the corresponding data acquisition terminal from the data acquisition module 102, and obtains the health risk level of the patient to which the corresponding data acquisition terminal belongs from the risk assessment module 103. The connection management module 101 is also used to obtain the connection priority and connection parameters of each data acquisition terminal from the connection evaluation module 104, and to configure the corresponding connection parameters for the data acquisition terminal with higher connection priority based on the connection priority of each data acquisition terminal.

[0036] In some embodiments, the connection management module 101 is specifically used for: Scan the wireless connection devices of the data acquisition terminal and find the data acquisition terminal in a connectable state; Complete the pairing, authentication, and registration of the data acquisition terminal to establish a data transmission channel between the wireless gateway and the data acquisition terminal; Assign a unique identifier to each data acquisition terminal and maintain the binding relationship between the data acquisition terminal and the patient and hospital bed; It can store the dimensions of physiological parameters collected by each data acquisition terminal (such as electrocardiogram, blood pressure, blood oxygen, body temperature, etc.), and can also store metadata such as manufacturer information, model information, and supported protocol versions of each data acquisition terminal. Provides an external port for querying the status (online / offline / fault) of data acquisition terminals; Manage the maintenance and release of wireless connections with connected data acquisition terminals; Configure connection parameters for the data acquisition terminal; It provides a unified data transmission and reception port to the outside world.

[0037] In some embodiments, the data acquisition module 102 obtains physiological parameters collected by each data acquisition terminal from the connection management module 101 through a unified data transceiver port provided by the connection management module 101. These data acquisition terminals include, but are not limited to, blood pressure monitors, electrocardiogram monitors, pulse oximeters, temperature patches, and infusion pumps.

[0038] In some embodiments, the risk assessment module 103 may have a built-in risk assessment model, such as a multi-channel time series model. Specifically, after acquiring the physiological parameters collected by each data acquisition terminal, the risk assessment module 103 aggregates the physiological parameters of the same patient within a preset time window (e.g., 5 minutes, 10 minutes, or other time windows) according to the patient identifier carried in the physiological parameters, and uses this aggregated parameter as input to the multi-channel time series model to obtain the corresponding patient's health risk level. The multi-channel time series model is a pre-trained model; for example, a large number of patients' multi-dimensional physiological parameters and corresponding health risk level labels can be used as training samples to train the multi-channel time series model. The health risk level labels of the patients used for training can be labeled by doctors or other experts.

[0039] In some embodiments, the connectivity evaluation module 104 may have a built-in connectivity evaluation model, such as a reinforcement learning-based connectivity evaluation model, or a supervised learning model based on gradient boosting trees or neural networks. For a reinforcement learning-based connectivity evaluation model, the optimal connectivity parameters can be automatically learned using the real-time performance, average latency, and packet loss rate of physiological parameter sampling as reward functions. For a supervised learning model based on gradient boosting trees or neural networks, the optimal connectivity parameters can be predicted based on experience with historical wireless connectivity parameters.

[0040] Based on this, in addition to the dimensional identifiers of the physiological parameters collected by the corresponding data acquisition terminal and the health risk level of the patient to which the corresponding data acquisition terminal belongs, the input of the connection assessment model, i.e., the assessment feature data, may also include the historical wireless connection quality indicators of the corresponding data acquisition terminal. These historical wireless connection quality indicators can be stored in the wireless gateway's memory in the form of a database table. It is understood that this memory is equipped with a data read / write port, and the connection assessment module 104 can obtain the historical wireless connection quality indicators of the corresponding data acquisition terminal from this memory through the data read / write port. Simultaneously, the connection management module 101 can write the wireless connection quality indicators of each connected data acquisition terminal into this memory.

[0041] Taking a Bluetooth gateway as an example, historical wireless connection quality metrics may include, but are not limited to, packet loss rate, retransmission count, average latency, and connection drop count. Evaluation feature data may also include the current load of the wireless gateway, such as processor utilization, memory usage, and the number of connected data acquisition terminals. Specifically, the processor utilization and memory usage in the current load of the wireless gateway can be obtained by calling the statistical data of the wireless gateway's hardware resource monitoring or management system through a preset data interface, while the number of connected data acquisition terminals can be obtained from the real-time statistical data of the connection management module 101. Therefore, when the evaluation feature data also includes the current load of the wireless gateway, the input of the connection evaluation module 104 is also connected to the output of the connection management module 101.

[0042] The output of the connectivity evaluation model is the connectivity priority and connectivity parameters of each data acquisition terminal; taking the wireless gateway as a Bluetooth gateway as an example, the connectivity parameters may include, but are not limited to, link connection interval, scanning window, transmit power, idle hold time, etc.

[0043] Therefore, after training, the connectivity assessment model can generate optimal connectivity parameters for data acquisition terminals corresponding to patients with high health risk levels and data acquisition terminals with important physiological parameter dimensions, so as to ensure the real-time performance and stability of physiological parameters collected by data acquisition terminals corresponding to important physiological parameter dimensions for patients with high health risk levels.

[0044] The importance of physiological parameter dimensions can be preset. For example, for the current patient, the important physiological parameter dimensions can be determined based on the patient's symptoms. Specifically, at least one important physiological parameter dimension corresponding to the symptoms can be determined based on expert experience; or a pre-trained AI model can be used to determine at least one important dimension corresponding to the symptoms. Specifically, symptoms labeled with important physiological dimension parameters can be used as training samples to train an AI model for determining at least one important dimension corresponding to a symptom. Then, using the trained AI model, the current patient's symptoms can be used as input to determine the current patient's important physiological parameter dimensions. The physiological parameter dimensions corresponding to the symptoms can be stored in the wireless gateway's memory. The connection evaluation model can first, based on the current patient's symptoms, call the important physiological parameter dimensions matching the current patient's symptoms from the memory as the current patient's important physiological parameter dimensions.

[0045] In summary, the wireless gateway provided in this application embodiment, by setting up a connection management module 101, discovers and connects to data acquisition terminals in a connectable state, and limits the number of data acquisition terminals corresponding to the same patient to collect different dimensions of physiological parameters. The data acquisition module 102 acquires physiological parameters carrying patient and dimension identifiers, the risk assessment module 103 determines the health risk level of each patient based on the physiological parameters, and then the connection assessment module 104 acquires assessment feature data including at least the dimension identifier and the patient's health risk level. Based on this assessment feature data, the connection priority and connection parameters of each data acquisition terminal are determined, and finally, the connection management module 101 prioritizes connection based on the connection priority. Higher-priority data acquisition terminals are configured with corresponding connection parameters. In the "one wireless gateway, multiple terminals" deployment mode, the connection priority and connection parameters of different data acquisition terminals can be dynamically adjusted according to the patient's health risk level and dimension identifier. This allows data acquisition terminals corresponding to patients with higher health risk levels, as well as data acquisition terminals corresponding to more important physiological parameter dimensions of the same patient, to obtain higher connection priority and better connection parameter configurations. This solves the technical problem in related technologies where it is difficult to meet the differentiated connection needs of different data acquisition terminals due to the use of fixed connection strategies. It realizes intelligent scheduling of wireless gateway resources and improves the real-time performance and reliability of key physiological parameter acquisition.

[0046] In some embodiments, the risk assessment module 103 is specifically used for: The physiological parameters collected by each data acquisition terminal are obtained from the data acquisition module 102; Based on the patient identifier carried by the physiological parameters, the corresponding physiological parameters of each patient are determined. Based on the physiological parameters corresponding to each patient, a health risk score is determined for each patient; Based on each patient's health risk score, the health risk level of each patient is determined.

[0047] In some embodiments, the risk assessment module 103 is specifically used to perform the following operations: First, physiological parameters collected by each data acquisition terminal are obtained from the data acquisition module 102. These physiological parameters include, but are not limited to, one or more of the following: electrocardiogram (ECG) data, blood pressure data, blood oxygen saturation data, body temperature data, respiratory rate data, and blood glucose data. Each physiological parameter carries a corresponding patient identifier and dimension identifier. The patient identifier uniquely identifies the patient to whom the physiological parameter belongs, while the dimension identifier indicates the specific type of the physiological parameter (e.g., ECG, blood pressure, blood oxygen, etc.).

[0048] Secondly, based on the patient identifiers carried by the physiological parameters, the acquired physiological parameters are aggregated according to the patient dimension to determine the set of physiological parameters corresponding to each patient. For example, for patient A, the set of physiological parameters may include electrocardiogram data, blood pressure data, and blood oxygen data; for patient B, the set of physiological parameters may include blood pressure data and body temperature data.

[0049] Then, based on the physiological parameters corresponding to each patient, a built-in risk assessment model is used to determine the health risk score for each patient. This health risk score can be a continuous value (e.g., 0-100 points) to quantify the degree of health risk for the patient. In specific assessments, assessment sub-models can be set for different types of physiological parameters: for example, for electrocardiogram (ECG) data, an arrhythmia detection model is used to assess the ECG risk sub-score; for blood pressure data, a blood pressure fluctuation analysis model is used to assess the blood pressure risk sub-score; then, the risk sub-scores are weighted and fused to obtain a comprehensive health risk score. The weights for the weighted fusion can be pre-set according to the importance of the physiological parameter dimensions; for example, the weight of ECG data is higher than the weight of body temperature data.

[0050] Finally, based on each patient's health risk score, the health risk level of each patient is determined according to a preset risk level classification rule. The risk level can be discrete, such as high risk, medium risk, low risk, or a more granular level of 1-5. The risk level classification rule can be a threshold comparison method: when the health risk score is greater than the first threshold, it is determined to be a high risk level; when the health risk score is less than or equal to the first threshold but greater than the second threshold, it is determined to be a medium risk level; when the health risk score is less than or equal to the second threshold, it is determined to be a low risk level.

[0051] In this way, the risk assessment module 103 can transform multi-source, multi-dimensional physiological parameters into a unified, quantifiable health risk level, providing an accurate decision-making basis for the subsequent connection assessment module 104 to determine the connection priority of each data acquisition terminal.

[0052] In some embodiments, the signal output terminal of the risk assessment module 103 is also connected to the signal input terminal of the connection management module 101; Risk assessment module 103 is also used for: In response to the fact that the health risk score of the first patient is greater than the risk score threshold, the risk data acquisition terminal corresponding to the physiological parameters that are at risk is determined based on the physiological parameters collected by each data acquisition terminal corresponding to the first patient. Correspondingly, the connection management module 101 is also used for: Shorten the link connection interval in the connection parameters of the risk data collection terminal.

[0053] In some embodiments, the signal output terminal of the risk assessment module 103 is connected to the signal input terminal of the connection management module 101 to form a rapid risk response path.

[0054] Specifically, after calculating the health risk score for each patient, the risk assessment module 103 monitors in real time whether there are any patients whose health risk scores exceed a preset risk score threshold. When the health risk score of the first patient is detected to be greater than the risk score threshold, it indicates that the first patient's health condition is abnormal or showing a deteriorating trend, requiring closer monitoring of the first patient's relevant physiological parameters.

[0055] At this point, the risk assessment module 103 further analyzes the physiological parameters collected by each data acquisition terminal corresponding to the first patient, identifying the physiological parameters with risks and their corresponding risky data acquisition terminals. The physiological parameters with risks may include: Physiological parameters that exceed the normal range, such as a sudden increase in blood pressure, a sudden drop in blood oxygen saturation, or abnormal heart rate; Physiological parameters with abnormal trends, such as persistently elevated blood pressure over multiple consecutive sampling periods; Physiological parameters that deviate significantly from historical baselines, such as current blood pressure values ​​that deviate from the patient's historical average blood pressure values ​​by more than a preset threshold.

[0056] For example, suppose the first patient is simultaneously connected to four data acquisition terminals: an electrocardiogram (ECG) monitor, a blood pressure monitor, a pulse oximeter, and a body temperature patch. When the health risk score calculated by the risk assessment module 103 exceeds a threshold, further analysis reveals that the patient's ECG data shows signs of arrhythmia, while the blood pressure, pulse oximeter, and body temperature are within the normal range. Therefore, the risk assessment module 103 determines that the physiological parameter posing a risk is the ECG data, and the corresponding risk data acquisition terminal is the ECG monitor.

[0057] After identifying the risk data acquisition terminal, the risk assessment module 103 sends an adjustment instruction to the connection management module 101 through its direct connection path. This adjustment instruction includes at least the identifier of the risk data acquisition terminal and the suggested type of connection parameter to be adjusted.

[0058] Accordingly, after receiving the adjustment instruction, the connection management module 101 shortens the link connection interval in the connection parameters of the risk data acquisition terminal, specifically as follows: If it is a Bluetooth connection, shorten the link connection interval, for example, from 100ms to 20ms; If it is a Wi-Fi connection, shorten the data reporting cycle, for example, from once every minute to once every 10 seconds; If other wireless connection technologies are used, their data transmission cycles will be adjusted accordingly.

[0059] By shortening the link connection interval, the wireless gateway can acquire risky physiological parameters more densely, thereby capturing changes in the patient's condition more promptly and providing more comprehensive data support for subsequent risk assessment and clinical intervention. Simultaneously, this direct connection path design avoids the path delay caused by the signal needing to pass through the connection assessment module 104 before being forwarded to the connection management module 101, enabling rapid response to risk events.

[0060] In addition, the risk assessment module 103 can continuously track changes in the first patient's health risk score during subsequent monitoring periods. When the first patient's health risk score falls below the risk score threshold and remains stable for a period of time, the risk assessment module 103 can send a recovery command to the connection management module 101 to restore the link connection interval of the risk data acquisition terminal to a normal level, so as to avoid unnecessary resource consumption.

[0061] Through the above mechanism, this embodiment realizes dynamic link connection interval adjustment based on real-time risk assessment, automatically strengthening the monitoring of key physiological parameters when the patient's condition is abnormal, which not only ensures medical safety, but also optimizes the resource utilization efficiency of the wireless gateway.

[0062] In some embodiments, the wireless gateway further includes a local alarm module, the signal input terminal of which is connected to the signal output terminal of the risk assessment module 103; The local alarm module is used to issue visual and / or auditory alarms in response to the first patient's health risk score being greater than the risk score threshold; the visual and / or auditory alarms carry physiological parameters indicating the risk.

[0063] In some embodiments, when the risk assessment module 103 detects that the health risk score of the first patient is greater than a preset risk score threshold, it sends an alarm trigger signal and related risk information to the local alarm module. This risk information includes, but is not limited to: the first patient's patient identifier, the dimension identifier of the physiological parameters at risk (such as ECG, blood pressure, blood oxygen, etc.), the specific abnormal physiological parameters, the health risk score, and the time of the risk event.

[0064] After receiving risk information, the local alarm module issues an alarm locally on the wireless gateway. The alarm method includes at least one of the following: Visual alarms: If the wireless gateway (such as a tablet-shaped wireless gateway) is equipped with a display screen, the alarm information will be displayed prominently on the screen. Specific forms of visual alarms may include: a pop-up alarm window on the display screen showing text such as "Patient (patient name, bed number) has an abnormality; ECG data is abnormal; please pay immediate attention." Auditory alarm: The wireless gateway emits an audible alarm through its built-in speaker or an external alarm device.

[0065] The local alarm module can also create alarm logs in local storage, recording detailed information for each alarm, including: the patient identifier that triggered the alarm, health risk score, physiological parameters indicating risk, and alarm time. These alarm records can be used for subsequent patient review, medical quality control, or as a reference for clinical decision-making.

[0066] Through the aforementioned local alarm mechanism, this embodiment can notify medical staff in a clear and conspicuous manner on the wireless gateway as soon as the patient's health risk increases, and accurately present detailed information on the physiological parameters of the risk, thus buying valuable time for clinical emergency treatment, while also mitigating the network delay or interruption risks that may exist with cloud alarms.

[0067] In some embodiments, the data acquisition module 102 further includes a protocol adaptive parsing submodule. The signal input terminal of the protocol adaptive parsing submodule serves as the signal input terminal of the data acquisition module 102 and is connected to the signal output terminal of the connection management module 101. The signal output terminal of the protocol adaptive parsing submodule serves as the signal output terminal of the data acquisition module 102 and is connected to the signal input terminal of the risk assessment module 103. The protocol adaptive parsing submodule is used for: The system obtains the raw physiological parameters collected by each data acquisition terminal from the connection management module 101, and queries the local template library to see if there is a first parsing template corresponding to the device model based on the device model carried in the raw physiological parameters. If it exists, the original physiological parameters are parsed using the first parsing template to obtain the physiological parameters collected by each data acquisition terminal; If it does not exist, the original physiological parameters are analyzed for protocol features to generate a second parsing template corresponding to the device model, and the second parsing template is stored in the local template library; the original physiological parameters are parsed using the second parsing template to obtain the physiological parameters collected by each data acquisition terminal.

[0068] In some embodiments, the protocol adaptive parsing submodule performs the following operations: First, obtain the raw physiological parameters and the device model of the data acquisition terminal corresponding to the raw physiological parameters; Specifically, the protocol adaptive parsing submodule obtains the raw physiological parameters collected by each data acquisition terminal from the connection management module 101. The raw physiological parameters are usually in the form of binary data frames, and the protocol formats used by data acquisition terminals from different manufacturers and different device models are different; the frame header, frame tail definition, field length, field order, data type encoding, and verification method of the raw physiological parameter data frames collected by data acquisition terminals of different device models are also different.

[0069] Then, the protocol adaptive parsing submodule queries the local template library; Specifically, the protocol adaptive parsing submodule maintains a local template library, which can store parsing templates corresponding to the device model using the device model as the key. The parsing templates are used to define how to parse the raw binary data frames into meaningful physiological parameters (such as systolic blood pressure, diastolic blood pressure, heart rate, blood oxygen saturation, etc.).

[0070] Once the device model of the data acquisition terminal is obtained, the protocol adaptive parsing submodule uses the device model as the query condition to search the local template library for whether there is a first parsing template corresponding to the device model.

[0071] If a first parsing template exists in the local template library, the protocol adaptive parsing submodule directly calls the first parsing template to parse the raw physiological parameters into structured physiological parameters. For example, for a certain model of ECG monitor, its first parsing template defines the data frame format as: frame header (2 bytes) + heart rate (2 bytes) + systolic blood pressure (2 bytes) + diastolic blood pressure (2 bytes) + timestamp (4 bytes) + checksum (1 byte). The protocol adaptive parsing submodule then extracts each field sequentially from the original data frame according to this format and converts it into standard format physiological parameters: {"heart rate":72,"systolic blood pressure":118,"diastolic blood pressure":76,"timestamp":"2024-03-11 14:30:25"}.

[0072] If the first parsing template is not found in the local template library, it indicates that the device model is a new device type that is connecting to the wireless gateway for the first time. In this case, the protocol adaptive parsing submodule automatically triggers the protocol adaptive parsing process to generate a second parsing template for the device model.

[0073] Based on this, in some embodiments, when a first parsing template corresponding to the device model does not exist in the local template library, the protocol adaptive parsing submodule is specifically used for: The raw physiological parameters within a preset time window are obtained, and the protocol features of the raw physiological parameters are analyzed to obtain the initial parsing template corresponding to the device model of the data acquisition terminal that collects the raw physiological parameters. The protocol features include at least the length distribution features of the data frame, the length pattern features of each field, the distribution features of the bits, the periodic structure features of the data frame, and the features of the check field. In response to the calibration operation, the initial parsing template is calibrated to obtain the second parsing template corresponding to the device model.

[0074] Specifically, the protocol adaptive parsing submodule first sets a preset time window to capture a sufficient number of raw physiological parameter data frames for protocol feature analysis of the raw physiological parameters. The length of this preset time window can be dynamically adjusted according to the device type and data transmission frequency of the data acquisition terminal, for example: For high-frequency data acquisition terminals (such as electrocardiogram monitors, which transmit dozens of data frames per second), the time window can be set to 30 seconds to 1 minute; for medium-frequency data acquisition terminals (such as blood pressure monitors, which take 1-2 measurements per minute), the time window can be set to 5-10 minutes; for low-frequency data acquisition terminals (such as body temperature patches, which report data every 5-10 minutes), the time window can be set to 30-60 minutes.

[0075] The protocol adaptive parsing submodule performs protocol feature analysis on the original physiological parameters, including: length distribution feature analysis of data frames, length pattern feature analysis of each field, bit distribution feature analysis, periodic structure feature analysis of data frames, and feature analysis of the check field.

[0076] Then, the protocol adaptive parsing submodule generates an initial parsing template based on the protocol feature analysis results from the five dimensions mentioned above. Since protocol feature analysis is based on statistics and inference, the generated initial parsing template may contain misjudgments or inaccuracies. Therefore, the protocol adaptive parsing submodule can also provide a human-machine interface, or use the human-machine interface provided by the wireless gateway, for engineers, technicians, and others to calibrate the initial parsing template.

[0077] After calibration is completed and the template is confirmed to be correct, the second analytical template corresponding to the device model can be obtained.

[0078] The generated second parsing template can then be stored in a local template library and used to parse the raw physiological parameters to obtain structured physiological parameters. Simultaneously, all subsequent raw physiological parameters received from the data acquisition terminal corresponding to this device model will be parsed in real time using the second parsing template.

[0079] Through the aforementioned protocol adaptive parsing and template calibration mechanism, this embodiment achieves rapid adaptation to data acquisition terminals of new device models, greatly shortens the traditional manual protocol analysis work, significantly improves the scalability and ease of use of the wireless gateway, and ensures the accuracy of protocol parsing.

[0080] In some embodiments, the wireless gateway further includes a data quality assessment module, the signal input terminal of which is connected to the signal output terminal of the data acquisition module 102, and the signal output terminal of the data quality assessment module is connected to the signal input terminal of the connection management module 101, or / and the alarm module. The data quality assessment module is used for: The physiological parameters collected by each data acquisition terminal are obtained from the data acquisition module 102; Based on the physiological parameters collected by each data acquisition terminal, output the data quality score of each physiological parameter; In response to a data quality score for a first physiological parameter falling below a data quality score threshold, the type of data anomaly event that caused the data quality score for the first physiological parameter to fall below the data quality score threshold is determined. If the data anomaly event type is a link event, then a connection parameter adjustment suggestion for the first data acquisition terminal is sent to the connection management module 101 based on the link event; or / and, if the data anomaly event type is a non-link event, then an alarm message for the non-link event is sent to the alarm module.

[0081] The first data acquisition terminal refers to the data acquisition terminal that acquires the first physiological parameter. The connection parameter adjustment suggestion is used to trigger the connection management module 101 to adjust the connection parameters associated with the first data acquisition terminal and the link event, so as to improve the link quality between the first data acquisition terminal and the wireless gateway.

[0082] Alarm messages are used to indicate that the primary physiological parameter needs to be re-collected or to prompt manual intervention.

[0083] In some embodiments, for physiological parameters of different dimensions, the data quality assessment module employs corresponding data quality assessment models to score the data quality of the corresponding physiological parameters. The data quality score is a quantitative indicator, such as a value between 0 and 1 or a score between 0 and 100, with higher scores indicating better data quality.

[0084] The specific process for scoring data quality for different dimensions of physiological parameters is as follows: For example, for the physiological parameters corresponding to electrocardiogram data, the data quality assessment module can analyze the following quality characteristics of the corresponding physiological parameters: Electrode detachment characteristics: If the physiological parameters corresponding to the ECG data remain at the baseline level for a long time without significant fluctuations, or the fluctuation amplitude is close to zero, it is judged as electrode detachment or poor contact, and the data quality score is reduced. Electromyographic interference characteristics: Analyze the high-frequency components of the physiological parameters corresponding to the ECG data. If the amplitude of the high-frequency noise is too large and exceeds the frequency range of the physiological parameters corresponding to the normal ECG data, it is judged as electromyographic interference, and the data quality score is reduced. Based on the detection results of electrode detachment characteristics and electromyographic interference characteristics, the system outputs a data quality score for the physiological parameters corresponding to the ECG data, and can attach specific data quality problem labels, such as electrode detachment, severe electromyographic interference, etc.

[0085] Understandably, for any physiological parameter dimension, the wireless gateway's memory can store the type of data anomaly event corresponding to that physiological parameter dimension. These data anomaly events include at least linked events and non-linked events. Linked events indicate that an event where the data quality score of the first physiological parameter is lower than a data quality score threshold is caused by a linked quality problem. Non-linked events indicate that an event where the data quality score of the first physiological parameter is lower than a data quality score threshold is caused by a non-linked quality problem (such as sensor damage at the data acquisition terminal, improper measurement posture, etc.). Taking ECG data as an example, electrode detachment is a non-linked event, while severe electromyography interference is a linked event.

[0086] Understandably, when the data quality score of the first physiological parameter is lower than the data quality score threshold, it indicates that the data quality of the first physiological parameter is too low, the first physiological parameter is unreliable, and measures need to be taken to improve the data quality of the first physiological parameter. Specifically, in response to the data quality score of the first physiological parameter being lower than the data quality score threshold, the data quality assessment module, based on the dimension of the first physiological parameter and the data quality problem label, retrieves the type of data anomaly event corresponding to the dimension of the first physiological parameter from the memory of the wireless gateway, and determines that the data anomaly event type corresponding to the data quality problem label of the first physiological parameter is a link event, and / or a non-link event.

[0087] If the data anomaly event type is a link event, the data quality assessment module sends a connection parameter adjustment suggestion for the first data acquisition terminal to the connection management module 101 based on the link event. For example, if the data quality label of the first physiological parameter is severe electromyography interference, then the data anomaly event type is a link event. In this case, the data quality assessment module can send a connection parameter adjustment suggestion associated with the first data acquisition terminal to the connection management module 101 to trigger the connection management module 101 to adjust the connection parameters of the first data acquisition terminal. The specific connection parameter adjustment suggestion can be "increase the transmission power of the first data acquisition terminal and shorten the connection interval from 30ms to 15ms" to improve the connection stability of the first data acquisition terminal.

[0088] Or / and, if the data anomaly event type is a non-link event, the data quality assessment module sends an alarm message for the non-link event to the alarm module; for example, if the data quality label for the first physiological parameter is electrode detachment, then the data anomaly event type is a non-link event, and the data quality assessment module can send "Electrode detachment, check and fix the electrode, and remeasure" to the alarm module.

[0089] For the physiological parameters corresponding to blood pressure data, the data quality assessment module can analyze features such as improper measurement posture and cuff leakage; for the physiological parameters corresponding to blood oxygen data, the data quality assessment module can analyze features such as finger movement and fiber optic interference; these will not be elaborated further in this embodiment.

[0090] Through the aforementioned data quality assessment and feedback mechanism, this embodiment achieves real-time monitoring and proactive optimization of the quality of physiological parameter collection. It can automatically adjust the connection parameters of the corresponding data collection terminal when the quality of physiological parameters declines, and / or issue alarm information through the alarm module, thereby improving the reliability and accuracy of physiological parameter collection, providing higher quality data input for subsequent health risk assessment, and reducing false alarms or missed alarms caused by data quality issues.

[0091] In some embodiments, the wireless gateway further includes a remote interaction module; the signal input terminal of the remote interaction module is connected to the signal output terminal of the data acquisition module 102 and the risk assessment module 103, and the signal output terminal of the remote interaction module is connected to the signal input terminal of the risk assessment module 103 and the connection assessment module 104. The remote interaction module is used to upload physiological parameters collected by each data acquisition terminal and the health risk level of each patient to the business server. The remote interaction module is also used to: send updated information from the business server to the risk assessment module 103 and / or the connection assessment module 104; wherein the updated information is used to update the first assessment strategy of the risk assessment module 103 and / or the second assessment strategy of the connection assessment module 104.

[0092] In some embodiments, the content uploaded by the remote interaction module can have multiple options. It can upload all physiological parameters and the health risk level of each patient to the business server in real time, or upload only the changed data or newly added risk events to reduce network usage. It can also implement tiered uploading according to the patient's health risk level, uploading data of high-risk patients in real time and uploading data of low-risk patients in batches at regular intervals.

[0093] In some embodiments, the upload triggering mechanism can be either timed uploads at fixed time intervals or risk event-triggered uploads, where an upload is made immediately when any patient's health risk level is detected to be elevated, or on-demand uploads in response to query requests from the business server.

[0094] In some embodiments, the first assessment strategy of updating the risk assessment module 103 may refer to updating the parameters of the risk assessment model built into the risk assessment module 103; similarly, the second assessment strategy of updating the connection assessment module 104 may refer to updating the parameters of the connection assessment model built into the connection assessment module 104.

[0095] In some embodiments, the wireless gateway further includes a human-computer interaction module; the signal input terminal of the human-computer interaction module is connected to the signal output terminal of the data acquisition module 102 and the risk assessment module 103. The human-computer interaction module is used to display the physiological parameters collected by each data acquisition terminal and the health risk level of each patient on the business display device.

[0096] In summary, the wireless gateway provided in this application embodiment has the following advantages compared with wireless gateways in related technologies: First, it achieves differentiated allocation of connection resources based on patients' health risk levels. Specifically, the risk assessment module 103 determines the health risk level of each patient based on physiological parameters; then, the connection assessment module 104 acquires assessment feature data, including at least dimensional identifiers and the patient's health risk level, and determines the connection priority and connection parameters of each data acquisition terminal based on this assessment feature data; finally, the connection management module 101 configures corresponding connection parameters for data acquisition terminals with higher priority based on the connection priority. This mechanism enables data acquisition terminals corresponding to patients with higher health risk levels to obtain higher connection priority and better connection parameter configurations, ensuring that the physiological parameters of critically ill patients can be collected and transmitted reliably and preferentially, solving the technical problem in related technologies where fixed connection strategies are difficult to meet the differentiated connection needs of different patients.

[0097] Second, it enables the differentiation of importance and resource allocation for different dimensions of physiological parameters for the same patient. Specifically, through the dimension identifier carried by the physiological parameters, the connection evaluation module 104 can identify the specific type of physiological parameter collected by each data acquisition terminal (such as ECG, blood pressure, blood oxygen, body temperature, etc.) and comprehensively consider the importance of that dimension when determining the connection priority. For the same patient, data acquisition terminals corresponding to more important physiological parameter dimensions (such as ECG and blood oxygen) can obtain higher connection priority, thereby ensuring the real-time performance and reliability of critical vital sign data are prioritized when wireless resources are limited.

[0098] Third, a dynamic resource scheduling closed loop driven by risk event feedback was constructed. Specifically, through the direct connection between the risk assessment module 103 and the connection management module 101, rapid response to risk events was achieved: when the health risk score of the first patient exceeds the risk score threshold, the risk assessment module 103, based on the physiological parameters collected by each data acquisition terminal corresponding to the first patient, identifies the risky data acquisition terminal corresponding to the risky physiological parameters and triggers the connection management module 101 to shorten the link connection interval of that risky data acquisition terminal. This mechanism enables the wireless gateway to automatically strengthen the monitoring of key physiological parameters when the patient's condition deteriorates, providing more sufficient data support for clinical intervention, while avoiding risk response delays caused by network latency or untimely cloud processing.

[0099] Fourth, it enables plug-and-play access for multiple manufacturers and models of devices. Specifically, through the protocol adaptive parsing submodule, when a new device model is connected to the data acquisition terminal, the local template library is queried based on the device model carried in the original physiological parameters. If no corresponding parsing template exists, the protocol characteristics of the original physiological parameters are analyzed to generate an initial parsing template. In response to the calibration operation, the parsing template corresponding to the device model is obtained and stored in the local template library. This mechanism enables the wireless gateway to automatically adapt to the private communication protocols of different manufacturers and models of data acquisition terminals, eliminating the need for engineers to manually write parsing code for each new device model. This significantly reduces the access cost and time for new device models of data acquisition terminals and improves the scalability and compatibility of the wireless gateway.

[0100] Fifth, it enables real-time monitoring and proactive optimization of physiological parameter acquisition quality. Specifically, the data quality assessment module scores the physiological parameters collected by each data acquisition terminal. When the data quality score of a certain physiological parameter falls below a preset data quality score threshold, it sends a connection parameter adjustment suggestion associated with that data acquisition terminal to the connection management module 101, and / or issues an alarm message through the alarm module. This mechanism can proactively optimize wireless connection parameters to address quality issues such as electrode detachment, signal interference, and unstable connections, improving the reliability and accuracy of data acquisition and providing higher-quality data input for subsequent health risk assessments.

[0101] Sixth, a local risk alarm mechanism is provided to enhance system robustness. Specifically, through the local alarm module, when the health risk score of the first patient exceeds the risk score threshold, a visual and / or auditory alarm is directly issued locally on the wireless gateway, and the alarm carries the physiological parameters indicating the risk. This mechanism ensures that medical staff can still obtain patient risk information in a timely manner in the event of network interruption or cloud delay, making up for the security risks of traditional cloud-based alarm solutions and significantly improving the reliability of the medical monitoring system.

[0102] Seventh, cloud-based collaboration and continuous strategy optimization are achieved. Specifically, the physiological parameters collected by each data acquisition terminal and the health risk level of each patient are uploaded to the business server through the remote interaction module. Simultaneously, the module receives update information from the business server to update the first assessment strategy of the risk assessment module 103 and the second assessment strategy of the connection assessment module 104. This mechanism enables the wireless gateway to obtain better model parameters and policy rules trained on large sample data from the cloud, allowing the assessment accuracy and scheduling efficiency of the wireless gateway to continuously improve with data accumulation and algorithm iteration.

[0103] In summary, the wireless gateway provided in this application, through multi-module collaboration and closed-loop control, achieves intelligent scheduling of differentiated connection resources based on the patient's health risk level and physiological parameters in a "one wireless gateway, multiple terminals" deployment mode. This solves the technical problems in related technologies, such as fixed connection strategies, inability to meet differentiated needs, high cost of new device access, inability to guarantee data quality, and excessive reliance on the cloud. It significantly improves the resource utilization efficiency of the wireless gateway and the reliability and real-time performance of medical data collection in multi-patient, multi-device scenarios.

[0104] Figure 2 This is a flowchart illustrating the wireless connectivity optimization method provided in an embodiment of this application.

[0105] like Figure 2 As shown, based on the wireless gateway provided in this application embodiment, this application embodiment also provides a wireless connection optimization method, including the following steps: Step 201: Discover and connect to the data acquisition terminal that is in a connectable state; In this case, the same patient corresponds to multiple data acquisition terminals, and the multiple data acquisition terminals collect physiological parameters of the patient in different dimensions respectively; Step 202: Obtain the physiological parameters collected by each data acquisition terminal; Among them, physiological parameters carry patient and dimensional identifiers; Step 203: Determine the health risk level of each patient based on physiological parameters; Step 204: Obtain the evaluation feature data of each data acquisition terminal, and determine the connection priority and connection parameters of each data acquisition terminal based on the evaluation feature data; The assessment feature data includes at least the dimension identifiers of the physiological parameters collected by the corresponding data acquisition terminal, and the health risk level of the patient to which the corresponding data acquisition terminal belongs. Step 205: Based on the connection priority of each data acquisition terminal, configure the corresponding connection parameters for the data acquisition terminal with higher connection priority.

[0106] In one embodiment, the health risk level of each patient is determined based on physiological parameters, including: Based on the patient identifier carried by the physiological parameters, the corresponding physiological parameters of each patient are determined. Based on the physiological parameters corresponding to each patient, a health risk score is determined for each patient; Based on each patient's health risk score, the health risk level of each patient is determined.

[0107] In some embodiments, after determining the health risk score of each patient, the wireless connection optimization method provided in this application further includes: In response to the fact that the health risk score of the first patient is greater than the risk score threshold, the risk data acquisition terminal corresponding to the physiological parameters that are at risk is determined based on the physiological parameters collected by each data acquisition terminal corresponding to the first patient. Shorten the link connection interval in the connection parameters of the risk data collection terminal.

[0108] In some embodiments, after determining the health risk score of each patient, the wireless connection optimization method provided in this application further includes: In response to the first patient's health risk score being greater than the risk score threshold, a visual and / or auditory alarm is issued; the visual and / or auditory alarm carries physiological parameters indicating the risk.

[0109] In some embodiments, step 202 includes the following steps: Obtain the raw physiological parameters collected by each data acquisition terminal, and based on the device model carried in the raw physiological parameters, query the local template library to see if there is a first parsing template corresponding to the device model; If it exists, the original physiological parameters are parsed using the first parsing template to obtain the physiological parameters collected by each data acquisition terminal; If it does not exist, the original physiological parameters are analyzed for protocol features to generate a second parsing template corresponding to the device model, and the second parsing template is stored in the local template library; the original physiological parameters are parsed using the second parsing template to obtain the physiological parameters collected by each data acquisition terminal.

[0110] In some embodiments, protocol feature analysis is performed on the raw physiological parameters to generate a second parsing template corresponding to the device model, including: The raw physiological parameters within a preset time window are obtained, and the protocol features of the raw physiological parameters are analyzed to obtain the initial parsing template of the device model of the data acquisition terminal that collects the raw physiological parameters. The protocol features include at least the length distribution features of the data frame, the length pattern features of each field, the distribution features of the bits, the periodic structure features of the data frame, and the features of the check field. In response to the calibration operation, the initial parsing template is calibrated to obtain the second parsing template corresponding to the device model.

[0111] In some embodiments, after step 202, the wireless connection optimization method provided in this application further includes: Based on the physiological parameters collected by each data acquisition terminal, output the data quality score of each physiological parameter; In response to a data quality score for a first physiological parameter falling below a data quality score threshold, the type of data anomaly event that caused the data quality score for the first physiological parameter to fall below the data quality score threshold is determined. If the data anomaly event type is a link event, the connection parameters of the first data acquisition terminal are adjusted based on the link event to improve the stability of the first data acquisition terminal connection; wherein, the first data acquisition terminal refers to the data acquisition terminal that collects the first physiological parameter; Or / and, if the data anomaly event type is a non-linked event, then an alarm message is issued for the non-linked event; the alarm message is used to prompt that the first physiological parameter needs to be re-collected or to prompt manual intervention.

[0112] In some embodiments, after determining the health risk level of each patient, the wireless connection optimization method provided in this application further includes: The physiological parameters collected by each data acquisition terminal and the health risk level of each patient are uploaded to the business server; The updated information from the business server is sent to the risk assessment module and / or the connection assessment module; wherein the updated information is used to update the first assessment strategy of the risk assessment module and / or the second assessment strategy of the connection assessment module.

[0113] In some embodiments, after determining the health risk level of each patient, the wireless connection optimization method provided in this application further includes: The physiological parameters collected by each data acquisition terminal and the health risk level of each patient are displayed on the business display device.

[0114] It should be noted that the terms "first," "second," etc., used in the specification, claims, and accompanying drawings of this disclosure are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this disclosure described herein can be implemented in orders other than those illustrated or described herein. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this disclosure. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this disclosure as detailed in the appended claims.

[0115] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "illustrative embodiment," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with an embodiment or example is included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0116] Any description of operation or method in the flowchart or otherwise described herein can be understood as representing a module, segment, or portion of code comprising one or more executable instructions for implementing a particular logical function or operation, and the scope of the preferred embodiments of the invention includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as will be understood by those skilled in the art to which embodiments of the invention pertain.

[0117] Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention.

Claims

1. A wireless gateway, characterized in that, The wireless gateway includes: A connection management module is used to discover and connect to data acquisition terminals that are in a connectable state; wherein, the same patient corresponds to multiple data acquisition terminals, and the multiple data acquisition terminals respectively collect physiological parameters of the patient in different dimensions; The data acquisition module is used to obtain physiological parameters collected by each of the data acquisition terminals from the connection management module; the physiological parameters carry patient identifiers and dimension identifiers. The risk assessment module is used to obtain physiological parameters collected by each of the data acquisition terminals from the data acquisition module, and to determine the health risk level of each patient based on the physiological parameters. The connection assessment module is used to obtain assessment feature data of each data acquisition terminal from the data acquisition module and the risk assessment module, and determine the connection priority and connection parameters of each data acquisition terminal based on the assessment feature data; wherein, the assessment feature data includes at least the dimension identifier of the physiological parameters collected by the corresponding data acquisition terminal, and the health risk level of the patient to which the corresponding data acquisition terminal belongs. The connection management module is further configured to obtain the connection priority and connection parameters of each data acquisition terminal from the connection evaluation module, and based on the connection priority of each data acquisition terminal, configure the corresponding connection parameters for the data acquisition terminal with higher connection priority.

2. The wireless gateway according to claim 1, characterized in that, The risk assessment module is specifically used for: The physiological parameters collected by each of the data acquisition terminals are obtained from the data acquisition module; Based on the patient identifier carried by the physiological parameters, determine the physiological parameters corresponding to each patient; Based on the physiological parameters corresponding to each patient, a health risk score is determined for each patient. Based on the health risk scores of each patient, the health risk level of each patient was determined.

3. The wireless gateway according to claim 2, characterized in that, The signal output terminal of the risk assessment module is also connected to the signal input terminal of the connection management module; The risk assessment module is also used for: In response to the first patient's health risk score being greater than the risk score threshold, the risk data acquisition terminal corresponding to the physiological parameter with risk is determined based on the physiological parameters collected by each of the data acquisition terminals corresponding to the first patient. Accordingly, the connection management module is also used for: Shorten the link connection interval in the connection parameters of the risk data collection terminal.

4. The wireless gateway according to claim 3, characterized in that, The wireless gateway also includes a local alarm module, the signal input terminal of which is connected to the signal output terminal of the risk assessment module; The local alarm module is used to issue a visual and / or auditory alarm in response to the first patient's health risk score being greater than a risk score threshold; the visual and / or auditory alarm carries the physiological parameters indicating the presence of risk.

5. The wireless gateway according to any one of claims 1 to 4, characterized in that, The data acquisition module further includes a protocol adaptive parsing submodule. The signal input terminal of the protocol adaptive parsing submodule serves as the signal input terminal of the data acquisition module and is connected to the signal output terminal of the connection management module. The signal output terminal of the protocol adaptive parsing submodule serves as the signal output terminal of the data acquisition module and is connected to the signal input terminal of the risk assessment module. The protocol adaptive parsing submodule is used for: The module obtains the raw physiological parameters collected by each data acquisition terminal from the connection management module, and queries the local template library to see if there is a first parsing template corresponding to the device model based on the device model carried in the raw physiological parameters. If they exist, the original physiological parameters are parsed using the first parsing template to obtain the physiological parameters collected by each of the data acquisition terminals; If it does not exist, then the original physiological parameters are analyzed for protocol features to generate a second parsing template corresponding to the device model, and the second parsing template is stored in the local template library; The original physiological parameters are parsed using the second parsing template to obtain the physiological parameters collected by each of the data acquisition terminals.

6. The wireless gateway according to claim 5, characterized in that, When no first parsing template corresponding to the device model exists in the local template library, the protocol adaptive parsing submodule is specifically used for: The raw physiological parameters within a preset time window are acquired, and protocol feature analysis is performed on the raw physiological parameters to obtain an initial parsing template corresponding to the device model of the data acquisition terminal that acquired the raw physiological parameters; the protocol features include at least the length distribution features of the data frame, the length pattern features of each field, the bit distribution features, the periodic structure features of the data frame, and the features of the check field; In response to the calibration operation, the initial parsing template is calibrated to obtain a second parsing template corresponding to the device model.

7. The wireless gateway according to claim 4, characterized in that, The wireless gateway further includes a data quality assessment module, the signal input terminal of which is connected to the signal output terminal of the data acquisition module, and the signal output terminal of which is connected to the connection management module and / or the signal input terminal of the alarm module. The data quality assessment module is used for: The physiological parameters collected by each of the data acquisition terminals are obtained from the data acquisition module; Based on the physiological parameters collected by each of the data acquisition terminals, output the data quality score of each of the physiological parameters; In response to a data quality score for a first physiological parameter falling below a data quality score threshold, the type of data anomaly event that caused the data quality score for the first physiological parameter to fall below the data quality score threshold is determined. If the data anomaly event type is a link event, then a connection parameter adjustment suggestion for the first data acquisition terminal is sent to the connection management module based on the link event; Or / and, if the data anomaly event type is a non-link event, then send alarm information for the non-link event to the alarm module; Wherein, the first data acquisition terminal refers to the data acquisition terminal that acquires the first physiological parameter; the connection parameter adjustment suggestion is used to trigger the connection management module to adjust the connection parameters associated with the first data acquisition terminal and the link event, so as to improve the link quality between the first data acquisition terminal and the wireless gateway; the alarm information is used to prompt that the first physiological parameter needs to be re-acquired or to prompt manual intervention.

8. The wireless gateway according to any one of claims 1 to 4, characterized in that, The wireless gateway further includes a remote interaction module; the signal input terminal of the remote interaction module is connected to the signal output terminal of the data acquisition module and the risk assessment module, and the signal output terminal of the remote interaction module is connected to the signal input terminal of the risk assessment module and the connection assessment module. The remote interaction module is used to upload the physiological parameters collected by each of the data acquisition terminals and the health risk level of each patient to the business server. The remote interaction module is further configured to: send the update information of the business server to the risk assessment module and / or the connection assessment module; wherein the update information is used to update the first assessment strategy of the risk assessment module and / or update the second assessment strategy of the connection assessment module.

9. The wireless gateway according to any one of claims 1 to 4, characterized in that, The wireless gateway also includes a human-computer interaction module; the signal input terminal of the human-computer interaction module is connected to the signal output terminals of the data acquisition module and the risk assessment module. The human-computer interaction module is used to display the physiological parameters collected by each data acquisition terminal and the health risk level of each patient on the business display device.

10. A wireless connection optimization method, characterized in that, The method includes: Discover and connect to data acquisition terminals that are in a connectable state; wherein, the same patient corresponds to multiple data acquisition terminals, and the multiple data acquisition terminals respectively collect physiological parameters of the patient in different dimensions; Acquire physiological parameters collected by each of the data acquisition terminals; the physiological parameters carry patient identifiers and dimension identifiers; Based on the physiological parameters, the health risk level of each patient was determined; The evaluation feature data of each of the data acquisition terminals is obtained, and the connection priority and connection parameters of each of the data acquisition terminals are determined based on the evaluation feature data; wherein, the evaluation feature data includes at least the dimension identifier of the physiological parameters collected by the corresponding data acquisition terminal, and the health risk level of the patient to which the corresponding data acquisition terminal belongs. Based on the connection priority of each data acquisition terminal, the corresponding connection parameters are configured preferentially for the data acquisition terminal with the higher connection priority.

Images