Postpartum rehabilitation monitoring system

Abstract

The invention discloses a postpartum rehabilitation monitoring system. The postpartum rehabilitation monitoring system comprises a sensor network, the sensor network comprises a center node and a plurality of sensor nodes connected in a net shape, the center node is connected with each sensor node, data collected by each sensor node corresponds to different data importance degrees, and each sensor node is connected with a corresponding sensor; the center node is used for summarizing all sensor data to obtain target sensor data; the power supply network is connected with the sensor and used for intelligently supplying power to the sensor; and the monitoring equipment is coupled with the central node and is used for analyzing the target sensor data transmitted by the central node to obtain a monitoring result. By means of the mode, data from different sensors are efficiently fused, a comprehensive health report is generated, and intelligent fusion analysis of multi-source data is achieved.

Description

Technical Field

[0001] The present application relates to the technical field of postpartum rehabilitation monitoring, and in particular to a postpartum rehabilitation monitoring system. Background Art

[0002] During the postpartum recovery process, mothers need to monitor their physiological status in all aspects to ensure the effectiveness and safety of recovery. However, traditional monitoring methods have the following problems: 1. A single sensor cannot fully reflect the physical condition of the mother: A single type of sensor cannot provide sufficient physiological information and cannot fully monitor the complex health status of the mother.

[0003] 2. The problem of time synchronization of data from different sensors: In a multi-sensor system, the sampling time of each sensor is not synchronized, resulting in inaccurate data or disordered timing, affecting the accuracy of comprehensive analysis.

[0004] 3. Data noise interference leads to insufficient monitoring accuracy: Sensor data is greatly affected by environmental interference, such as temperature, humidity, electromagnetic interference and other factors, which leads to a decrease in data accuracy.

[0005] 4. Limited multi-source data fusion and analysis capabilities: The existing system finds it difficult to effectively combine data from multiple sensors, making it difficult to comprehensively analyze and judge the recovery of parturients.

[0006] 5. Difficulty in predicting the trend of changes in physiological indicators: Single data is difficult to support trend prediction and difficult to identify potential risks in advance. Summary of the invention

[0007] The postpartum rehabilitation monitoring system provided in this application can solve at least one of the above-mentioned technical problems.

[0008] In the first aspect, the present application provides a postpartum rehabilitation monitoring system, which includes: a sensor network, the sensor network includes a central node and multiple sensor nodes connected in a mesh, the central node is connected to each sensor node, the data collected by each sensor node corresponds to different data importance levels, and each sensor node is connected to the corresponding sensor; the central node is used to aggregate all sensor data to obtain target sensor data; a power supply network, connected to the sensor, for intelligently supplying power to the sensor; a monitoring device, coupled to the central node, for analyzing the target sensor data transmitted by the central node to obtain monitoring results.

[0009] Among them, each sensor node is used to preprocess the sensor data, extract features, and perform preliminary anomaly detection, and control the sampling frequency and accuracy of its corresponding sensor according to the dynamic change characteristics of the sensor data.

[0010] Among them, multiple sensor nodes connected in a mesh include a first sensor node, a second sensor node, a third sensor node, a fourth sensor node and a fifth sensor node; the first sensor node is connected to the second sensor node, the first sensor node is connected to the third sensor node, the second sensor node is connected to the fourth sensor node, the third sensor node, the fourth sensor node and the fifth sensor node are connected; the data of the first sensor node is the most important.

[0011] Among them, the first sensor node is used to extract features of the corresponding sensor data and use the machine learning model to identify the extracted features. The second sensor node is used to perform pattern recognition on the corresponding sensor data and use an adaptive sampling algorithm to obtain sampling data, and control the sampling frequency and accuracy of the corresponding sensor according to the sampling data. The third sensor node is used to compress the corresponding sensor data.

[0012] Among them, the central node is also used to perform Kalman filtering and probability mapping on the sensor data of the first sensor node and the fourth sensor node, and use the hybrid association algorithm to perform multi-hypothesis tracking, and fuse the tracked sensor data, and perform predictive analysis on the fused sensor data; the central node is also used to process the outliers of the sensor data of the second sensor node, and use the machine learning model to measure the uncertainty of the sensor data after the outlier processing, and use the graph matching algorithm to perform feature matching, and then perform anomaly detection on the matched features; the central node is also used to extract features from the sensor data of the third sensor node, and use the Bayesian inference engine to calibrate the confidence of the features, and then perform semantic association.

[0013] Among them, the power supply network includes: an energy collection module, which is used to collect thermoelectric energy, piezoelectric energy, bioelectric energy, photovoltaic energy and vibration energy; an autonomous power adjustment module, connected to the energy collection module, and used to dynamically adjust the parameters of the energy collection module; a power management module, connected to the autonomous power adjustment module, the energy collection module and several sensors, and used to convert the energy collected by the energy collection module to obtain electrical energy, and use the electrical energy to power several sensors.

[0014] Among them, end-to-end encrypted transmission is adopted between sensor networks, power supply networks and monitoring equipment, and blockchain technology is used to process data.

[0015] Among them, wireless communication technology is used between the sensor network, power supply network and monitoring equipment.

[0016] Among them, multi-channel adaptive frequency hopping is performed between the sensor network, the power supply network and the monitoring equipment.

[0017] Among them, the monitoring equipment is also used to analyze the target sensor data using a deep learning model to obtain monitoring results, and to evaluate the target sensor data using fuzzy reasoning to obtain the health status of the monitored person, and to predict the changing trend of physiological indicators based on the target sensor data.

[0018] The beneficial effects of the present application are: Different from the prior art, the postpartum rehabilitation monitoring system provided by the present application can achieve high-precision synchronization when multiple sensors collect data to ensure the time consistency of the data. And use corresponding algorithms to reduce noise interference, improve data reliability, and improve the anti-interference ability of sensor data: and efficiently integrate data from different sensors to generate comprehensive health reports and realize intelligent fusion analysis of multi-source data. Using algorithms to predict changes in physiological indicators and prevent potential health risks, it can accurately predict the trend of changes in physiological indicators. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. Among them: Figure 1 It is a structural schematic diagram of an embodiment of a postpartum rehabilitation monitoring system provided by the present application; Figure 2 It is a structural diagram of an embodiment of a sensor network provided by the present application; Figure 3 It is a structural schematic diagram of an embodiment of a power supply network provided in the present application. DETAILED DESCRIPTION

[0020] The technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. It will be understood that the specific embodiments described herein are only used to explain the present application, rather than to limit the present application. It should also be noted that, for ease of description, only some but not all structures related to the present application are shown in the drawings. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of the present application.

[0021] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0022] See also Figure 1 , Figure 1 1 is a schematic diagram of a structure of an embodiment of a postpartum rehabilitation monitoring system provided by the present application. The postpartum rehabilitation monitoring system 100 includes: a sensor network 10, a power supply network 20 and a monitoring device 30. In some embodiments, the postpartum rehabilitation monitoring system 100 can be deployed locally and in the cloud.

[0023] The sensor network 10 includes a central node and a plurality of sensor nodes connected in a mesh. The central node is connected to each sensor node. The data collected by each sensor node corresponds to different data importance levels, and each sensor node is connected to a corresponding sensor. The central node is used to aggregate all sensor data to obtain target sensor data.

[0024] In some embodiments, sensors may include micro temperature, bioimpedance, pressure and near-infrared spectrum sensors that can comprehensively capture the temperature, muscle condition, blood flow and other physiological parameters of the parturient; as well as gyroscope sensors and accelerometers that can capture the movement and position status of the parturient; and photovoltaic, ambient temperature, humidity and air pressure sensors that can obtain changes in the use environment.

[0025] Furthermore, time division multiplexing technology is used to collect sensor data at different time intervals to avoid data conflicts. Microsecond-level synchronous sampling technology is used to achieve high-precision time alignment of sensor data to ensure the timing consistency between data.

[0026] Among them, each sensor node is used to preprocess the sensor data, extract features, and perform preliminary anomaly detection, and control the sampling frequency and accuracy of its corresponding sensor according to the dynamic change characteristics of the sensor data.

[0027] In some embodiments, the sensor network 10 has a star network topology. In the star topology, all sensor data are aggregated and synchronously transmitted through a central node to ensure efficient data integration and management.

[0028] In some embodiments, a hybrid topology is introduced in the star network topology: (mainly for physiological sensors), where the star network retains the central node for data aggregation and processing. Mesh network local interconnection: direct communication links are established between key sensors to improve network redundancy and real-time performance. A hierarchical routing mechanism is used to dynamically select the optimal transmission path based on the importance of data and latency requirements.

[0029] For example, Figure 2 As shown, multiple sensor nodes connected in a mesh include a first sensor node, a second sensor node, a third sensor node, a fourth sensor node and a fifth sensor node; the first sensor node is connected to the second sensor node, the first sensor node is connected to the third sensor node, the second sensor node is connected to the fourth sensor node, and the third sensor node, the fourth sensor node and the fifth sensor node are connected; the data of the first sensor node is the most important.

[0030] For example, the data of the first sensor node is high priority data, the data of the second sensor node is standard data, the data of the third sensor node is low priority data, the data of the fourth sensor node 4 is backup data, and the data of the fifth sensor node is emergency data.

[0031] The central node includes an adaptive router, a central processor and a data center. The adaptive router is connected to the first sensor node, the second sensor node, the third sensor node, the fourth sensor node and the fifth sensor node, and is used to receive data from the first sensor node, the second sensor node, the third sensor node, the fourth sensor node and the fifth sensor node, and send the data to the central processor, which is processed by the central processor and stored in the data center, and selects a corresponding path according to the data priority to transmit the data of the data center. For example, the paths are divided into high priority paths, standard paths and backup paths.

[0032] Among them, the first sensor node is used to extract features of the corresponding sensor data and use the machine learning model to identify the extracted features. The second sensor node is used to perform pattern recognition on the corresponding sensor data and use an adaptive sampling algorithm to obtain sampling data, and control the sampling frequency and accuracy of the corresponding sensor according to the sampling data. The third sensor node is used to compress the corresponding sensor data.

[0033] Furthermore, in the sensor network 10, it can be divided into an edge computing layer, an adaptive perception layer, an energy management layer, a communication layer, and a security layer. The corresponding interconnected sensor nodes are in the edge computing layer, and corresponding feature extraction, data compression, and pattern recognition are performed on the sensor data.

[0034] The adaptive perception layer can set the corresponding machine learning model, pattern analysis engine and adaptive sampling algorithm.

[0035] The energy collection module, clock drift correction system, battery management module and autonomous power scheduling module in the energy management layer can be shared with the power supply network 20 .

[0036] The communication layer can be equipped with low-power wide area network, Bluetooth low energy, gateway, routing control module, etc.

[0037] Data encryption, identity authentication, access control, etc. can be performed in the security layer.

[0038] In some embodiments, the clock drift correction system includes a reference time source management layer, a distributed correction network, a clock feature analysis layer, a correction algorithm layer, and an intelligent error correction and fault tolerance layer.

[0039] In the reference time source management layer, the clock corresponding to the atomic clock is obtained, and then GPS synchronization is performed to obtain satellite time, and then quantum time synchronization is performed. The synchronized time is then sent to the distributed correction network for time synchronization of the central node and sensor node, and autonomous correction is performed in the autonomous correction unit and the correction result is sent to the gateway coordinator for time synchronization.

[0040] The clock feature analysis layer includes a clock deviation detection module for detecting clock deviation. It only involves frequency feature extraction, drift pattern recognition, and detection using machine learning models. The detection results are then sent to the correction algorithm layer, and the correction process is performed using the adaptive compensation algorithm in the correction algorithm layer. For example, a multidimensional correction strategy is generated using a predictive correction model, and the multidimensional correction strategy is filtered using a Kalman filter to obtain the final correction method.

[0041] The intelligent error correction and fault tolerance layer mainly performs anomaly detection, real-time compensation, and removal of multiple redundancies, and finally performs error recovery to achieve corresponding error correction and fault tolerance.

[0042] Among them, the central node is also used to perform Kalman filtering and probability mapping on the sensor data of the first sensor node and the fourth sensor node, and use the hybrid association algorithm to perform multi-hypothesis tracking, and fuse the tracked sensor data, and perform predictive analysis on the fused sensor data; the central node is also used to process the outliers of the sensor data of the second sensor node, and use the machine learning model to measure the uncertainty of the sensor data after the outlier processing, and use the graph matching algorithm to perform feature matching, and then perform anomaly detection on the matched features; the central node is also used to extract features from the sensor data of the third sensor node, and use the Bayesian inference engine to calibrate the confidence of the features, and then perform semantic association.

[0043] The power supply network 20 is connected to the sensor and is used to supply intelligent power to the sensor.

[0044] For example, Figure 3 As shown, the power supply network 20 includes: an energy collection module, an autonomous power adjustment module and a power management module.

[0045] The energy harvesting module is used to collect thermoelectric energy, piezoelectric energy, bioelectric energy, photovoltaic energy and vibration energy.

[0046] The autonomous power adjustment module is connected to the energy collection module and is used to dynamically adjust the parameters of the energy collection module. The autonomous power adjustment module can have real-time monitoring function, adaptive adjustment function, fault prediction function, and learning and optimization function. That is, the energy collection module and the power management module can be monitored in real time, and the parameters of the energy collection module and the power management module can be adjusted and the parameters of the power management module can be optimized by combining the adaptive adjustment function, the fault prediction function, the learning and optimization function, etc. The autonomous power adjustment module is also coupled to the sensor to adjust the parameters of the energy collection module and the power management module according to the state requirements fed back by the sensor.

[0047] The power management module is connected to the autonomous power adjustment module, the energy collection module and several sensors, and is used to convert the energy collected by the energy collection module to obtain electrical energy, and use the electrical energy to power several sensors. The power management module can have energy conversion function, energy storage management function, power distribution function and energy recovery function.

[0048] The energy storage management function can store excess electrical energy. The power distribution function can reasonably supply different powers to different sensors.

[0049] Among them, end-to-end encrypted transmission is adopted between the sensor network 10, the power supply network 20 and the monitoring device 30, and the blockchain technology is used to process the data. That is, the postpartum rehabilitation monitoring system 100 has a secure data management framework, which corresponds to a data usage policy and consent management module. In the data usage policy and consent management module, there is a user management unit for user management, a data rights exercise unit for data rights exercise management, and an end-to-end encrypted transmission unit. In addition, the end-to-end encrypted transmission unit involves the AES / RSA encryption protocol and the secure storage infrastructure. The secure storage infrastructure involves hybrid storage, access control and blockchain technology. In the blockchain technology, the data is hashed and stored, tampering detection and sensitive data anonymization are performed.

[0050] Sensitive data anonymization involves functions such as tokenization, differential privacy, user authentication and authorization. User authentication and authorization involves multi-factor authentication, role-based access control, security auditing and monitoring. Security auditing and monitoring involves regular security audits and real-time monitoring.

[0051] In some embodiments, the sensor network 10 , the power supply network 20 and the monitoring device 30 communicate with each other using wireless communication technology.

[0052] In some embodiments, multi-channel adaptive frequency hopping is performed between the sensor network 10 , the power supply network 20 , and the monitoring device 30 .

[0053] In some embodiments, the communication layer described above uses wireless communication technologies, such as low power wide area network (LoRaWAN), Bluetooth 5.0 low energy (BLE) communication, multi-channel adaptive frequency hopping and intelligent adaptive frequency hopping (AFH) technology.

[0054] Among them, the system modeling of the communication layer is as follows: State space: Contains multi-dimensional features such as the current channel, adjacent channel quality, and interference conditions.

[0055] Action space: selectable target frequency hopping channels.

[0056] Reward function: A design that comprehensively considers throughput, bit error rate, and energy efficiency to guide agent behavior optimization.

[0057] And the deep reinforcement learning framework includes: algorithms, neural network structures and experience replay.

[0058] In terms of algorithms, an action-value evaluation framework is used to generate actions and evaluate the value of actions.

[0059] The neural network structure can combine CNN, RNN, and fully connected layers. Among them, CNN, RNN, and fully connected layers are connected in sequence. That is, RNN is connected to the output layer of CNN, and the fully connected layer is connected to the output layer of RNN.

[0060] CNN is used to extract the spatial features of channel states.

[0061] RNN is used to capture the temporal correlation of channel states.

[0062] The fully connected layer is used to output the probability or value of the frequency hopping action.

[0063] Experience replay is used to store historical interaction data, improve sample utilization efficiency, and speed up learning.

[0064] For the adaptive frequency hopping strategy, the optimal channel can be selected based on value evaluation, and the agent can quickly adapt to changes in the dynamic environment. The reward function guides the agent to learn the optimal frequency hopping behavior.

[0065] The monitoring device 30 is coupled to the central node and is used to analyze the target sensor data transmitted by the central node to obtain monitoring results. The monitoring device 30 can be a cloud server or a local device, and can implement any data processing method, fusion method and analysis method in this application.

[0066] Among them, the monitoring device 30 is also used to use a deep learning model to analyze the target sensor data to obtain monitoring results, and use fuzzy reasoning to evaluate the target sensor data to obtain the health status of the monitored person, and predict the change trend of physiological indicators based on the target sensor data.

[0067] The data fusion algorithm in this application can adopt multi-level fusion architecture design, feature extraction based on deep learning, state evaluation based on fuzzy reasoning, and time series prediction model construction. In this application, the data fusion algorithm can be deployed simultaneously on the local + cloud.

[0068] The multi-level fusion architecture design can fuse data from the data layer, feature layer and decision layer to obtain a more comprehensive health status analysis.

[0069] Feature extraction based on deep learning can use deep learning models to automatically extract and learn the features of multidimensional data and improve the level of intelligent data analysis.

[0070] Status assessment based on fuzzy reasoning can use fuzzy reasoning to assess the health status of parturient women and provide a basis for decision making.

[0071] The construction of time series prediction models can be based on time series data to predict the changing trends of physiological indicators and provide early warning of possible health problems.

[0072] The signal processing method in this application can adopt adaptive wavelet transform denoising, interference identification based on Fourier analysis, data smoothing by Kalman filtering, and outlier detection and correction. The signal processing method in this application can be deployed simultaneously on the local + cloud.

[0073] Adaptive wavelet transform denoising can eliminate noise interference and retain effective signals through adaptive wavelet analysis.

[0074] Interference identification based on Fourier analysis can identify and filter out periodic interference signals through Fourier transform.

[0075] Kalman filtering data smoothing can use Kalman filtering technology to achieve data smoothing and improve data continuity and stability.

[0076] Outlier detection and correction can identify and correct outliers in data based on statistical methods to ensure the accuracy of monitoring data.

[0077] In some embodiments, the signal processing may be as follows: After the data is input, in the data preprocessing stage, outlier detection and correction, as well as adaptive wavelet transform denoising are performed on the input data. In the adaptive wavelet transform denoising process, machine learning can be used to optimize wavelet selection.

[0078] After data preprocessing, interference suppression is performed on the data. For example, Fourier analysis interference identification is performed on the preprocessed data, and adaptive filtering is performed on the identified data. The data is then enhanced, and the enhanced data is output and future trend prediction is performed. Among them, machine learning can be used to classify interference types.

[0079] In one application scenario, the postpartum rehabilitation monitoring system 100 may have a multimodal data platform that can collect sensor data from multiple sensors (such as physiological sensors, environmental sensors, motion sensors, etc.). And perform data fusion and analysis on these data. For example, data fusion and analysis can be feature layer extraction and analysis, and then use AI and deep learning analysis to obtain real-time health feedback. For example, data fusion and analysis can be data layer collection and synchronization, and then perform time series prediction and personalized insights, where personalized insights can be obtained by combining health problem prediction and intervention with health advice. For example, data fusion and analysis can be a decision layer and fuzzy reasoning to obtain health status assessment and personalized health decisions.

[0080] Personalized health decisions can be implemented in conjunction with interactive virtual health assistants. For example, personalized feedback and suggestions can be provided after implementation. Specifically, augmented reality and virtual reality can be used as virtual health assistants to achieve personalized health decisions. Augmented reality and virtual reality can achieve data visualization, immersive educational experience, and childbirth process simulation.

[0081] For example, interactive virtual health assistants can provide emotional support and consultation functions.

[0082] Another example is that interactive virtual health assistants can provide gamification incentives and interactions. Specifically, they can combine reward and incentive systems with healthy behavior guidance to complete personalized health decisions, achieve healthy behavior incentives and increase participation.

[0083] The postpartum rehabilitation monitoring system 100 provided in this application can be applied to the following scenarios: 1. Real-time monitoring in the postpartum recovery room: In the hospital's postpartum recovery ward, real-time physiological status monitoring is provided to facilitate doctors to adjust the recovery plan at any time.

[0084] 2. Monitoring of the rehabilitation training process: During the rehabilitation training process, the mother's physical condition is dynamically monitored to ensure that the rehabilitation training is safe and effective.

[0085] 3. Remote monitoring of home rehabilitation: It provides remote data transmission function, so doctors can check the mother’s physical condition at any time and provide guidance on rehabilitation plans.

[0086] 4. Postpartum complication warning: Based on the fusion analysis of multi-parameter data, the risk of postpartum complications is predicted in real time to provide protection for maternal health.

[0087] In summary, the postpartum rehabilitation monitoring system 100 provided in this application can achieve high-precision synchronization when multiple sensors collect data, ensure the time consistency of the data, and improve the accuracy of comprehensive analysis. And use corresponding algorithms to reduce noise interference, improve data reliability, and improve the anti-interference ability of sensor data: and efficiently integrate data from different sensors to generate comprehensive health reports and realize intelligent fusion analysis of multi-source data. Using algorithms to predict changes in physiological indicators and prevent potential health risks, it can accurately predict the trend of changes in physiological indicators.

[0088] In the several embodiments provided in this application, it should be understood that the disclosed methods and devices can be implemented in other ways. For example, the device implementation described above is only illustrative, for example, the division of the modules or units is only a logical function division, and there may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed.

[0089] If the integrated units in the above other embodiments are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) or a processor to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, RandomAccess Memory), disk or optical disk and other media that can store program codes.

[0090] The above description is only an implementation method of the present application, and does not limit the patent scope of the present application. Any equivalent structure or equivalent process transformation made using the contents of the present application specification and drawings, or directly or indirectly used in other related technical fields, are also included in the patent protection scope of the present application.

Claims

1. A postpartum rehabilitation monitoring system, characterized in that: The postpartum rehabilitation monitoring system comprises: A sensor network, the sensor network comprising a central node and a plurality of sensor nodes connected in a mesh, the central node being connected to each of the sensor nodes, the data collected by each sensor node corresponding to different data importance levels, and each sensor node being connected to a corresponding sensor; the central node being used to aggregate all sensor data to obtain target sensor data; A power supply network, connected to the sensor, for intelligently supplying power to the sensor; The monitoring device is coupled to the central node and is used to analyze the target sensor data transmitted by the central node to obtain monitoring results.

2. The postpartum rehabilitation monitoring system according to claim 1, characterized in that: Each sensor node is used to preprocess sensor data, extract features, and perform preliminary anomaly detection, and control the sampling frequency and accuracy of its corresponding sensor according to the dynamic change characteristics of the sensor data.

3. The postpartum rehabilitation monitoring system according to claim 2, characterized in that: The plurality of mesh-connected sensor nodes include a first sensor node, a second sensor node, a third sensor node, a fourth sensor node and a fifth sensor node; the first sensor node is connected to the second sensor node, the first sensor node is connected to the third sensor node, the second sensor node is connected to the fourth sensor node, and the third sensor node, the fourth sensor node and the fifth sensor node are connected; The data of the first sensor node has the highest importance.

4. The postpartum rehabilitation monitoring system according to claim 3, characterized in that: The first sensor node is used to extract features from the corresponding sensor data and identify the extracted features using a machine learning model. The second sensor node is used to perform pattern recognition on the corresponding sensor data and obtain sampling data using an adaptive sampling algorithm, and control the sampling frequency and accuracy of the corresponding sensor based on the sampling data. The third sensor node is used to perform data compression on the corresponding sensor data.

5. The postpartum rehabilitation monitoring system according to claim 3, characterized in that: The central node is also used to perform Kalman filtering and probability mapping on the sensor data of the first sensor node and the fourth sensor node, and use a hybrid association algorithm to perform multi-hypothesis tracking, and fuse the tracked sensor data, and perform predictive analysis on the fused sensor data; The central node is also used to perform outlier processing on the sensor data of the second sensor node, and use a machine learning model to measure uncertainty on the sensor data after the outlier processing, and use a graph matching algorithm to perform feature matching, and then perform anomaly detection on the matched features; The central node is also used to extract features from the sensor data of the third sensor node, and use the Bayesian inference engine to calibrate the confidence of the features, and then perform semantic association.

6. The postpartum rehabilitation monitoring system according to claim 1, characterized in that: The power supply network comprises: Energy harvesting module, used to harvest thermoelectric energy, piezoelectric energy, bioelectric energy, photovoltaic energy and vibration energy; An autonomous power adjustment module, connected to the energy collection module, and used to dynamically adjust the parameters of the energy collection module; The power management module is connected to the autonomous power adjustment module, the energy collection module and a plurality of sensors, and is used to convert the energy collected by the energy collection module to obtain electrical energy, and use the electrical energy to power the plurality of sensors.

7. The postpartum rehabilitation monitoring system according to claim 1, characterized in that: End-to-end encrypted transmission is adopted between the sensor network, the power supply network and the monitoring equipment, and blockchain technology is used to process data.

8. The postpartum rehabilitation monitoring system according to claim 1, characterized in that: The sensor network, the power supply network and the monitoring device communicate with each other using wireless communication technology.

9. The postpartum rehabilitation monitoring system according to claim 8, characterized in that: Multi-channel adaptive frequency hopping is performed between the sensor network, the power supply network and the monitoring device.

10. The postpartum rehabilitation monitoring system according to claim 1, characterized in that: The monitoring device is also used to analyze the target sensor data using a deep learning model to obtain monitoring results, and to evaluate the target sensor data using fuzzy reasoning to obtain the health status of the monitored person, and to predict the change trend of physiological indicators based on the target sensor data.

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