Distributed sensor self-healing system
By utilizing a distributed sensor self-healing system, digital twin and multimodal data fusion technologies, combined with federated learning and self-healing actuators, the reliability and maintenance cost issues of sensors in complex environments are solved, enabling efficient intelligent diagnosis and predictive maintenance.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-11-28
- Publication Date
- 2026-03-13
AI Technical Summary
Sensors are susceptible to environmental interference, aging, and failure in complex environments. Existing self-healing technologies are costly and lack predictive capabilities, resulting in insufficient system reliability and high maintenance costs.
A distributed sensor self-healing system is adopted, which constructs a virtual model through digital twin technology, integrates multimodal data, uses federated learning for collaborative optimization, realizes intelligent diagnosis and predictive maintenance, and performs dynamic correction and resource scheduling through hierarchical self-healing actuators.
It improves the reliability and sensing accuracy of sensor systems in complex environments, reduces maintenance costs, extends mean time between failures (MTBF), and enhances data privacy protection and system scalability.
Smart Images
Figure CN121655596A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of intelligent sensor system technology, and in particular to a distributed sensor self-healing system based on digital twins, multimodal data fusion, and federated learning. This system is suitable for complex scenarios requiring precise sensing and reliable monitoring of physical quantities, such as the Industrial Internet of Things, smart cities, environmental monitoring, healthcare, and smart homes. Background Technology
[0002] With the rapid development of IoT technology, sensor networks have been widely applied in various fields. As a bridge connecting the physical and digital worlds, the accuracy and reliability of sensors directly determine the performance of the entire system. However, in practical applications, sensors face multiple challenges:
[0003] 1. Environmental Interference Issues: Sensors are susceptible to environmental factors such as temperature fluctuations, humidity changes, electromagnetic interference, and mechanical vibration during use, causing the output value to deviate from the true physical quantity. For example, in industrial environments, high temperatures may cause thermal drift in temperature sensors; in outdoor monitoring, rain and dust can affect the accuracy of optical sensors.
[0004] 2. Sensor Aging and Failure: As electronic components, sensors experience performance degradation over time. Factors such as changes in resistance, aging of sensitive materials, and mechanical fatigue can all lead to decreased sensor accuracy or even complete failure. Traditional periodic calibration methods are inadequate for handling sudden malfunctions.
[0005] 3. Perception limitations in complex scenarios: Single-type sensors often have blind spots in complex environments. For example, in autonomous driving scenarios, camera performance degrades significantly in strong light or foggy weather, while LiDAR is affected by rain and snow. Systems lacking multi-sensor collaboration struggle to guarantee continuous and reliable perception.
[0006] 4. Shortcomings of existing self-healing technologies: Current sensor self-healing technologies have the following limitations:
[0007] Homogeneous redundancy dependency: It requires the deployment of multiple sensors of the same type to detect anomalies by comparing output values, which is costly and cannot cope with systemic interference.
[0008] The correction model is too simple: most of them use a single regression model or neural network for correction, and lack a hierarchical processing strategy for different fault types.
[0009] Lack of predictive capability: Existing systems mainly repair after a failure occurs, and cannot predict the trend of sensor performance degradation in advance.
[0010] Data silo problem: Each sensor system operates independently and cannot improve overall performance through cross-system learning.
[0011] Traditional manual calibration and maintenance methods require regular on-site operations by professionals. In large sensor networks, the cost and complexity of this maintenance method increase exponentially.
[0012] Therefore, there is an urgent need in this field for a new type of sensor system that can achieve intelligent diagnosis, predictive maintenance and adaptive repair, maintain high reliability and accuracy in complex environments, and reduce maintenance costs. Summary of the Invention
[0013] The purpose of this invention is to provide a distributed sensor self-healing system based on digital twins and multimodal fusion, addressing the problems of insufficient reliability, high maintenance costs, and lack of predictive capabilities in existing sensor systems in complex environments. By constructing digital twins of the sensor network, fusing multimodal sensing data, and employing federated learning for collaborative optimization, intelligent self-healing is achieved from the node level to the system level.
[0014] To achieve the above objectives, the present invention adopts the following technical solution: a distributed sensor self-healing system, characterized in that it comprises:
[0015] A sensor network deployed in physical space, the network comprising a variety of heterogeneous physical sensors; a digital twin engine for building and running a virtual digital twin model for the sensor network and the physical environment it monitors, the model being able to receive physical sensor data in real time and output simulated values of the system state;
[0016] A multimodal fusion diagnostic module, connecting the sensor network and the digital twin engine, is used to fuse data from the physical sensors, output values from the digital twin model, and external context data to diagnose the health status of the sensors and identify the root causes of anomalies.
[0017] A layered self-healing actuator dynamically selects and executes different correction strategies from the signal layer, data layer to the model layer based on the output of the multimodal fusion diagnostic module, in order to maintain the reliability of the overall perception function of the system.
[0018] The present invention further provides that the multimodal fusion diagnostic module further integrates a federated learning unit, which coordinates multiple edge nodes to perform distributed machine learning model training based on their local data, and aggregates the trained model parameters to update the global diagnostic and self-healing model, instead of centralizing the original data.
[0019] The present invention further provides that the digital twin engine integrates a physical mechanism model and a data-driven machine learning model, which can make short-term predictions. The system performs predictive self-healing operations based on the prediction results, including initiating calibration sequences in advance or reassigning sensing tasks.
[0020] The present invention further provides that the external context data includes at least one of the following from the information system: work schedule, maintenance log, weather forecast data, and power grid quality report. This data serves as a virtual reference for assisting in diagnosis.
[0021] The present invention further includes a signal layer correction strategy for the hierarchical self-healing actuator that includes using an adaptive filter with online parameter adjustment based on reinforcement learning; a data layer fusion strategy that includes using a feature-level fusion algorithm based on a convolutional neural network; and a model layer reconstruction strategy that includes using a generative adversarial network to generate virtual readings of the failed sensor based on relevant sensor data.
[0022] The present invention further includes a dynamic resource scheduler. When a sensor is diagnosed as having severely degraded performance or failure, the scheduler automatically reallocates all or part of the sensing tasks that should have been performed by that sensor to other functional sensors in the network that have sensing redundancy.
[0023] The present invention further provides that the federated learning unit employs differential privacy or homomorphic encryption technology to protect the data privacy of each edge node during the parameter aggregation process.
[0024] The present invention further provides that the system is deployed in a smart city traffic monitoring network, wherein the physical sensors include geomagnetic coils, cameras and radar, the digital twin model is a traffic flow model of urban roads, and the hierarchical self-healing actuator is used to fuse geomagnetic coil and radar data and reconstruct the traffic flow information of the area using the traffic flow model when the camera is blocked.
[0025] The present invention further provides that the system is deployed in a predictive maintenance scenario for industrial equipment, wherein the physical sensors include vibration sensors, acoustic sensors and infrared thermal imagers, and the multimodal fusion diagnostic module diagnoses bearing faults and determines whether the reading drift of adjacent temperature sensors is caused by the fault itself by fusing these heterogeneous data.
[0026] The present invention further provides a self-healing strategy blockchain audit module for generating tamper-proof records of key self-healing decision events, diagnostic basis, and correction results for auditing and traceability.
[0027] The beneficial effects of the present invention include: Compared with the prior art, the beneficial effects of the present invention include:
[0028] 1. Through a multi-layered fault diagnosis and repair mechanism, the system can maintain normal operation when sensors experience various types of faults. Experimental data shows that, under the same conditions, the mean time between failures (MTBF) of this system is more than three times that of traditional systems.
[0029] 2. The system can automatically complete most of the calibration and maintenance work, reducing the need for manual intervention. In actual deployment, it is expected to reduce maintenance costs by more than 60%, especially for widely distributed and large-scale sensor networks, where the economic benefits are even more significant.
[0030] 3. Through multimodal data fusion and digital twin technology, the system can distinguish between sensor malfunctions and environmental changes, maintaining accurate perception in complex and ever-changing environments. Tests show that under extreme environmental conditions, the perception accuracy of this system is more than 40% higher than that of traditional systems.
[0031] 4. Implement predictive maintenance
[0032] Based on the predictive capabilities of digital twins, the system can detect sensor performance degradation trends in advance, provide early warnings and interventions before failures occur, and avoid losses caused by sudden failures.
[0033] 5. By adopting a federated learning framework, all participants can jointly train and optimize the model without sharing the original data, which is particularly suitable for application scenarios involving privacy protection, such as healthcare and smart homes.
[0034] 6. The distributed architecture design allows the system to easily expand the sensor scale, and newly added sensor nodes can be quickly integrated into the self-healing system and share the system's learning results.
[0035] 7. By using blockchain technology to record the self-healing process, the system can provide auditable and traceable evidence for decision-making, thereby enhancing users' trust in the system. Attached image description:
[0036] Figure 1 This is a schematic diagram of the system framework of an embodiment of the present invention. Detailed Implementation
[0037] The embodiments of this application will be described in detail below, providing a clear and complete description of the technical solutions within this application. Obviously, the described embodiments are merely a portion of the embodiments of this application, and not all of them. The components of this application described and shown herein can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of this application. 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.
[0038] like Figure 1 As shown, the present invention is a distributed sensor self-healing system, comprising:
[0039] A sensor network deployed in physical space, the network comprising a variety of heterogeneous physical sensors; a digital twin engine for building and running a virtual digital twin model for the sensor network and the physical environment it monitors, the model being able to receive physical sensor data in real time and output simulated values of the system state;
[0040] A multimodal fusion diagnostic module, connecting the sensor network and the digital twin engine, is used to fuse data from the physical sensors, output values from the digital twin model, and external context data to diagnose the health status of the sensors and identify the root causes of anomalies.
[0041] A layered self-healing actuator dynamically selects and executes different correction strategies from the signal layer, data layer to the model layer based on the output of the multimodal fusion diagnostic module, in order to maintain the reliability of the overall perception function of the system.
[0042] The multimodal fusion diagnostic module further integrates a federated learning unit, which coordinates multiple edge nodes to train distributed machine learning models based on their local data, and aggregates the trained model parameters to update the global diagnostic and self-healing model, without centralizing the original data.
[0043] The digital twin engine integrates a physical mechanism model and a data-driven machine learning model, enabling short-term predictions. The system performs predictive self-healing operations based on the prediction results, including initiating calibration sequences in advance or reassigning sensing tasks. In this embodiment, the external context data includes at least one of the following: work schedules from the information system, maintenance logs, weather forecast data, and power grid quality reports. These data serve as virtual references to assist in diagnosis.
[0044] The signal layer correction strategy of the hierarchical self-healing actuator includes using an adaptive filter with online parameter adjustment based on reinforcement learning; the data layer fusion strategy includes using a feature-level fusion algorithm based on convolutional neural networks; and the model layer reconstruction strategy includes using a generative adversarial network to generate virtual readings of the failed sensor based on relevant sensor data.
[0045] In this embodiment, the system also includes a dynamic resource scheduler. When a sensor is diagnosed as having severely degraded performance or failure, the scheduler automatically reallocates all or part of the sensing tasks that should have been performed by that sensor to other normally functioning sensors in the network that have sensing redundancy.
[0046] The federated learning unit employs differential privacy or homomorphic encryption technology to protect the data privacy of each edge node during parameter aggregation.
[0047] The system is applicable to different scenarios, such as when deployed in a smart city traffic monitoring network. The physical sensors include geomagnetic coils, cameras, and radar. The digital twin model is a traffic flow model of urban roads. The hierarchical self-healing actuator is used to fuse geomagnetic coil and radar data and reconstruct the traffic flow information of the area using the traffic flow model when the camera is blocked.
[0048] In addition, the system is deployed in predictive maintenance scenarios for industrial equipment. The physical sensors include vibration sensors, acoustic sensors, and infrared thermal imagers. The multimodal fusion diagnostic module diagnoses bearing faults and determines whether the reading drift of adjacent temperature sensors is caused by the fault itself by fusing these heterogeneous data.
[0049] The system provides a self-healing strategy blockchain audit module, which generates tamper-proof records of key self-healing decision events, diagnostic basis, and correction results for auditing and traceability.
[0050] This invention employs a multi-layered fault diagnosis and repair mechanism, enabling the system to maintain normal operation even when sensors experience various types of faults. Experimental data shows that, under the same conditions, the system's mean time between failures (MTBF) is more than three times that of traditional systems. The system can automatically complete most calibration and maintenance tasks, reducing the need for manual intervention. In practical deployment, it is expected to reduce maintenance costs by more than 60%, especially for widely distributed and large-scale sensor networks, where the economic benefits are even more significant. Through multimodal data fusion and digital twin technology, the system can distinguish between sensor faults and environmental changes, maintaining accurate perception in complex and ever-changing environments. Tests show that under extreme environmental conditions, the system's perception accuracy is more than 40% higher than that of traditional systems. Based on the predictive capabilities of digital twins, the system can detect sensor performance degradation trends in advance, providing early warnings and interventions before faults occur, avoiding losses caused by sudden failures. Employing a federated learning framework, participating parties can collaboratively train and optimize models without sharing raw data, making it particularly suitable for applications involving privacy protection, such as healthcare and smart homes. The distributed architecture design allows the system to easily scale up the sensor base; newly added sensor nodes can quickly integrate into the self-healing system and share the system's learning results. By using blockchain technology to record the self-healing process, an auditable and traceable basis can be provided for system decision-making, thereby enhancing users' trust in the system.
[0051] If certain terms are used in the specification and claims to refer to specific components, those skilled in the art will understand that hardware manufacturers may use different names to refer to the same component. This specification and claims do not distinguish components based on differences in name, but rather on differences in function. The term "comprising" as used throughout the specification and claims is an open-ended term and should be interpreted as "comprising but not limited to." "Approximately" means that within an acceptable margin of error, those skilled in the art can solve the technical problem and substantially achieve the technical effect within a certain margin of error.
[0052] It should be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a product or system comprising a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a product or system. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the product or system that includes that element.
[0053] The foregoing description illustrates and describes several preferred embodiments of the present invention. However, as previously stated, it should be understood that the present invention is not limited to the forms disclosed herein and should not be construed as excluding other embodiments. It can be used in various other combinations, modifications, and environments, and can be altered within the scope of the inventive concept by means of the foregoing teachings or techniques or knowledge in related fields. Any modifications and variations made by those skilled in the art that do not depart from the spirit and scope of the present invention should be within the protection scope of the appended claims.
Claims
1. A distributed sensor self-healing system, characterized in that, include: A sensor network deployed in physical space, the network comprising a variety of heterogeneous physical sensors; A digital twin engine for building and running a virtual digital twin model of the sensor network and the physical environment it monitors, which can receive physical sensor data in real time and output simulated values of the system state; A multimodal fusion diagnostic module, connecting the sensor network and the digital twin engine, is used to fuse data from the physical sensors, output values from the digital twin model, and external context data to diagnose the health status of the sensors and identify the root causes of anomalies. A layered self-healing actuator dynamically selects and executes different correction strategies from the signal layer, data layer to the model layer based on the output of the multimodal fusion diagnostic module, in order to maintain the reliability of the overall perception function of the system.
2. The system as described in claim 1, characterized in that, The multimodal fusion diagnostic module further integrates a federated learning unit, which coordinates multiple edge nodes to train distributed machine learning models based on their local data, and aggregates the trained model parameters to update the global diagnostic and self-healing model, instead of centralizing the original data.
3. The system as described in claim 1, characterized in that, The digital twin engine integrates a physical mechanism model and a data-driven machine learning model, enabling short-term predictions. The system performs predictive self-healing operations based on the prediction results, including initiating calibration sequences in advance or reassigning sensing tasks.
4. The system as described in claim 1, characterized in that, The external context data includes at least one of the following from the information system: work schedule, maintenance logs, weather forecast data, and power grid quality reports. This data serves as a virtual reference to assist in diagnosis.
5. The system as described in claim 1, characterized in that, The signal layer correction strategy of the hierarchical self-healing actuator includes using an adaptive filter with online parameter adjustment based on reinforcement learning; the data layer fusion strategy includes using a feature-level fusion algorithm based on convolutional neural networks; and the model layer reconstruction strategy includes using a generative adversarial network to generate virtual readings of the failed sensor based on relevant sensor data.
6. The system as described in claim 1, characterized in that, The system also includes a dynamic resource scheduler. When a sensor is diagnosed as having severely degraded performance or has failed, the scheduler automatically reassigns all or part of the sensing tasks that should have been performed by that sensor to other functional sensors in the network that have sensing redundancy.
7. The system as described in claim 2, characterized in that, The federated learning unit employs differential privacy or homomorphic encryption technology to protect the data privacy of each edge node during parameter aggregation.
8. The system as described in claim 1, characterized in that, The system is deployed in a smart city traffic monitoring network. The physical sensors include geomagnetic coils, cameras, and radar. The digital twin model is a traffic flow model of urban roads. The hierarchical self-healing actuator is used to fuse geomagnetic coil and radar data and reconstruct traffic flow information of the area using the traffic flow model when the camera is blocked.
9. The system as described in claim 1, characterized in that, The system is deployed in predictive maintenance scenarios for industrial equipment. The physical sensors include vibration sensors, acoustic sensors, and infrared thermal imagers. The multimodal fusion diagnostic module diagnoses bearing faults and determines whether the reading drift of adjacent temperature sensors is caused by the fault itself by fusing these heterogeneous data.
10. The system as claimed in claim 1, characterized in that, The system provides a self-healing strategy blockchain audit module, which generates tamper-proof records of key self-healing decision events, diagnostic basis, and correction results for auditing and traceability.