Wood dual-ice layer distributed detection system and method based on federated learning algorithm

Abstract

The invention relates to the technical field of spaceflight detection, in particular to a federated learning algorithm-based wood two-ice-layer distributed detection system and a federated learning algorithm-based wood two-ice-layer distributed detection method. The system comprises a plurality of detection nodes and a central server, and the detection nodes are distributed on the surface of the second wood or below an ice layer; a federated learning model is arranged in the detection node and is used for detecting ice layer data of an area where the detection node is located, carrying out model training through a federated learning algorithm and periodically sending model parameters obtained through training to the central server; and the central server is in remote communication connection with the detection nodes, is used for carrying out collaborative updating according to the model parameters sent by the detection nodes in combination with a federated learning algorithm to obtain a global model, and is also used for obtaining ice layer data of the detection nodes according to the global model. According to the method, the data privacy and security of the system are enhanced, and the detection accuracy and depth are greatly improved.

Description

Technical Field

[0001] The present invention relates to the field of aerospace detection technology, and in particular to a distributed detection system and method for the Europa ice layer based on a federated learning algorithm. Background Art

[0002] As one of the celestial bodies in the solar system that is most likely to have oceans and life, the thickness of Europa's ice layer and the structure of its internal ocean have become the focus of scientific exploration. However, due to the wide coverage and uneven thickness of Europa's ice layer, and the extreme environment on its surface, which poses great challenges to the deployment and operation of detection equipment, traditional centralized detection systems are difficult to meet real-time and diversified requirements. It has the following main defects:

[0003] 1. Communication delay and bandwidth limitation: Europa is far away from the Earth, and the real-time transmission of detection data faces huge challenges. It often takes a long time to transmit the detection data back to the Earth for analysis, so the data transmission needs to be delayed for a long time. In addition, due to the communication bandwidth between the detector and the Earth, it is impossible to achieve real-time transmission of massive detection data, resulting in data processing lags. In addition, in the centralized processing mode, the data of all detection nodes needs to be transmitted to the central node for unified processing, which increases the risk of data leakage or attack, especially in extreme environments, where communication security is difficult to fully guarantee.

[0004] 2. Centralized processing performance bottleneck: The traditional centralized processing model relies on a single central node to process and analyze all data. As the amount of detection data increases, the computing and storage resources of the central node are prone to reaching bottlenecks, making it impossible to process multi-source heterogeneous data in a timely manner.

[0005] 3. Single point failure risk: The environment on the surface of Europa is extremely harsh, and the detection equipment may encounter mechanical failure or external damage. The traditional centralized architecture has the risk of single point failure. The failure of any node may affect the entire detection mission. Once the central node fails, the detection and data processing capabilities of the entire system will be seriously affected, which in turn affects the continuity and reliability of the mission.

[0006] 4. Lack of collaborative working capabilities: Europa’s ice layer covers a huge area with uneven thickness. The detection mission requires not only local detailed analysis but also wide-area coverage. The existing detection system usually operates independently at each detection node and lacks an effective multi-node coordination mechanism, making it difficult to achieve the goal of combining global data integration with local precise analysis of Europa’s ice layer.

[0007] Therefore, it is necessary to improve the existing Europa ice layer detection technology to solve the above-mentioned technical problems. Summary of the invention

[0008] In response to the technical problems in the prior art of Europa ice layer detection, such as large data transmission volume, low detection accuracy and precision, and high risk of single-point failure, the present invention proposes a distributed detection system and method for Europa ice layer based on a federated learning algorithm to solve at least one of the above technical problems.

[0009] In order to solve the above technical problems, the technical solution adopted by the present invention is: a distributed detection system of the Europa ice layer based on a federated learning algorithm, comprising:

[0010] Detection nodes: There are multiple detection nodes, which are distributed on the surface of Europa or under the ice layer. A federated learning model is set in each detection node to detect ice layer data in the area, train the model through a federated learning algorithm, and regularly send the trained model parameters to the central server.

[0011] Central server: The central server is remotely connected to the detection nodes for collaborative updating based on the model parameters sent by each detection node in combination with the federated learning algorithm to obtain a global model, and is also used to obtain ice layer data of each detection node based on the global model.

[0012] The detection node includes: a detection module, a training module and a communication module;

[0013] The detection module is used to detect ice data in the area;

[0014] The training module is used to perform model training through a federated learning algorithm based on the ice layer data obtained by the detection module;

[0015] The communication module is used to periodically send the obtained model parameters to the central server.

[0016] The data detected by the detection node include ice thickness, composition, and temperature.

[0017] The central server is further used to send the global model obtained through collaborative updating to each detection node, and the detection node is further used to adjust its detection strategy and parameters according to the received global model.

[0018] In addition, the present invention also provides a distributed detection method for the Europa ice layer based on a federated learning algorithm, which is implemented based on the detection system and includes the following steps:

[0019] Step 1: Deploy multiple detection nodes on the surface of Europa or under the ice layer; each detection node is equipped with an ice layer detection unit and pre-loaded with a local computing model;

[0020] Step 2: Each detection node collects ice data in its area;

[0021] Step 3: Each detection node uses the collected ice data to train the model locally using the federated learning algorithm, and each detection node regularly uploads the model parameters to the central server;

[0022] Step 4: The central server updates the model parameters uploaded by each detection node in combination with the federated learning algorithm to obtain a global model. The central server also obtains the ice data detection results of each detection node in each area based on the global model.

[0023] Step 5: The central server sends the collaboratively updated global model to each detection node, and each detection node adjusts its detection strategy and parameter settings according to the updated global model;

[0024] Step 6: Repeat steps 2 to 5 to continuously update the model parameters of each detection node and the global model of the central server, and obtain the ice layer data of each detection node according to the global model updated in real time by the central server.

[0025] In step 2, the ice layer data collected by each detection node includes: ice layer thickness, composition, and temperature.

[0026] The step 2 specifically includes the following steps: after the system is initialized, each detection node starts to work independently to collect ice layer data in the area.

[0027] Compared with the prior art, the present invention has the following beneficial effects:

[0028] 1. Less bandwidth consumption and more secure data: The present invention uses a federated learning algorithm to enable detection nodes to directly process ice layer data locally, greatly reducing transmission delays and bandwidth consumption, making it possible to make decisions faster and adapt to the complex environment of the dynamic changes in Europa's ice layer.

[0029] 2. Reduced performance requirements for the central server: The present invention utilizes a distributed system to dynamically adapt to environmental changes. Each detection node adjusts its own detection model based on real-time data and shares information through federated learning. The central server only needs to integrate the model parameter data of each detection node to form a global analysis result without having to process a large amount of raw ice layer data. This enables the detection system to flexibly respond to dynamic changes inside the ice layer, greatly reducing the performance requirements for the central server.

[0030] 3. The system has good robustness, and the detection range is wider and more accurate: The present invention forms a detection system through distributed detection nodes and a central server to ensure that each detection node can work independently. Even if some nodes fail, other nodes can still continue to perform ice detection tasks, thereby improving the system's reliability and task continuity in complex environments, and has good robustness.

[0031] 4. Strong collaborative working ability and strong adaptability to complex environments: The present invention uses distributed multi-node collaborative work to enable each node to cover different areas of the ice layer. The detection results of each node are integrated through federated learning to generate a global ice layer data model, which can achieve both large-scale coverage and local precision detection, thereby improving the overall efficiency of the detection task. Moreover, the present invention can dynamically adapt to environmental changes. Each detection node adjusts its own detection model according to real-time data, shares information through federated learning, and integrates to form a global analysis result, so that the detection system can flexibly respond to dynamic changes inside the ice layer. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 A schematic diagram of the structure of a distributed detection system for the Europa ice layer based on a federated learning algorithm provided in an embodiment of the present invention;

[0033] Figure 2 A schematic flow chart of a method for distributed detection of the Europa ice layer based on a federated learning algorithm provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0034] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all the embodiments; based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0035] Embodiment 1

[0036] like Figure 1 As shown, the first embodiment of the present invention provides a distributed detection system for the Europa ice layer based on a federated learning algorithm, including: a detection node and a central server. There are multiple detection nodes, which are distributed on the surface of Europa or under the ice layer; a federated learning model is set in the detection node, which is used to detect the ice layer data in the area, and to train the model through the federated learning algorithm, and to regularly send the trained model parameters to the central server; the central server is set on the earth, and is remotely connected to the detection node for collaborative update based on the model parameters sent by each detection node in combination with the federated learning algorithm to obtain a global model, and is also used to obtain the ice layer data of each detection node based on the global model.

[0037] Furthermore, if Figure 1As shown, in this embodiment, the detection node includes: a detection module, a training module and a communication module; the detection module is used to detect ice data in the area; the training module is used to perform model training through a federated learning algorithm based on the ice data obtained by the detection module; and the communication module is used to periodically send the obtained model parameters to the central server. Among them, the training module is loaded with a local calculation model. When the system is initialized, each detection node starts to work independently, collects relevant data such as ice thickness, composition, temperature, etc., and performs preliminary local analysis.

[0038] Specifically, in this embodiment, the detection node may be a fixed detector or a mobile detection device.

[0039] Specifically, in this embodiment, the data detected by the detection node includes relevant data such as ice thickness, composition, and temperature.

[0040] Furthermore, in this embodiment, the central server is further used to send the global model obtained by collaborative updating to each detection node, and the detection node is further used to adjust its detection strategy and parameters according to the received global model.

[0041] Embodiment 2

[0042] like Figure 2 As shown, Embodiment 2 of the present invention provides a distributed detection method for the Europa ice layer based on a federated learning algorithm, which is implemented based on the detection system described in Embodiment 1 and includes the following steps:

[0043] Step 1: Deploy multiple detection nodes on the surface of Europa or under the ice layer; each detection node is equipped with an ice detection unit and pre-loaded with a local computing model.

[0044] Multiple detection nodes are deployed on the surface of Europa or under the ice layer. These nodes can be fixed detectors or mobile detection equipment. Each node is equipped with an ice layer detection unit node and preloaded with a local computing model. When the system is initialized, each node starts to work independently, collects relevant data such as ice layer thickness, composition, temperature, etc., and performs preliminary local analysis.

[0045] Step 2: Each detection node collects ice data in its area.

[0046] In step 2, the ice layer data collected by each detection node includes: ice layer thickness, composition, and temperature.

[0047] The step 2 specifically includes the following steps: after the system is initialized, each detection node starts to work independently to collect ice layer data in the area.

[0048] Step 3: Each detection node uses the federated learning algorithm to train the model locally based on the ice layer data collected in real time, and each detection node regularly uploads the model parameters to the central server.

[0049] Each detection node collects ice data according to its area, and first analyzes and models the collected data through a local training module. This step ensures that each detection node can independently complete the preliminary ice feature extraction, reduce the amount of raw data transmission, and respond to local changes in real time. After completing local data processing, the detection node will use the federated learning algorithm to train the model. Each detection node will train its own detection model based on the local data set, without the need to share raw data. During the model training process, the node regularly uploads the model parameters (not data) to the shared central server for the central server to coordinate the update of the global model.

[0050] Step 4: The central server updates the model parameters uploaded by each detection node in combination with the federated learning algorithm to obtain a global model. Based on the global model, the central server obtains the ice data detection results of each detection node in each area.

[0051] In this embodiment, all detection nodes upload their trained model parameters to the central server, so that the model parameters can be shared with other detection nodes. In this embodiment, the central server integrates the model parameters uploaded by each detection node through the federated learning framework to generate a comprehensive global model. The model summarizes the detection results of each node in different areas, thereby obtaining a unified analysis result of the overall structure of Europa's ice layer.

[0052] Step 5: The central server sends the collaboratively updated global model to each detection node, and each detection node adjusts its detection strategy and parameter settings according to the updated global model.

[0053] The integrated global model is sent to each detection node, and each node adjusts its detection strategy and parameter settings according to the updated model. The system will continuously optimize the detection accuracy based on the new model, improve the detection ability of each node in its local area, and form a distributed detection network with dynamic updates and real-time feedback.

[0054] Step 6: Repeat steps 2 to 5 to continuously update the model parameters of each detection node and the global model of the central server, and obtain the ice layer data of each detection node according to the global model updated in real time by the central server.

[0055] Part of the code implemented by the present invention is as follows:

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[0059]

[0060] In summary, the present invention provides a distributed detection system and method for the Europa ice layer based on a federated learning algorithm, the feasibility of which has been proven through a series of simulations. It has at least the following advantages:

[0061] 1. The present invention adopts the federated learning algorithm as the core technology, and realizes the collaborative training and model sharing between distributed detection nodes through the algorithm, avoiding the centralized transmission of raw data. This not only reduces the bandwidth requirements and communication delays, but also only transmits model parameters instead of raw data in the federated learning framework, avoiding the risk of detection data being intercepted or tampered with during transmission, and enhancing the data privacy and security of the system. It is especially suitable for environments such as Europa where remote communication conditions are limited, and also ensures data privacy and security.

[0062] 2. The present invention adopts a distributed detection system, and each detection node operates independently and is responsible for the detection tasks in the area it covers. Each detection node has autonomous processing capabilities, can locally process and analyze ice layer detection data, and dynamically update the local model based on the collected data to ensure the real-time and accuracy of the detection; each detection node regularly exchanges model parameters through federated learning to achieve global information integration, thereby obtaining an overall detection and analysis of the Europa ice layer on the basis of local processing. The distributed architecture effectively improves the stability of the system and the continuity of the task. Even if some nodes fail, other nodes can still operate normally, so it can adapt to the complex and changeable detection environment of Europa.

[0063] 3. The present invention can cover a large area of ​​Europa's ice layer through the collaborative work of multiple detection nodes, and integrate the local detection results of each node to form a global analysis of the ice layer, greatly improving the accuracy and depth of detection.

[0064] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A distributed detection system for Europa's ice layer based on a federated learning algorithm, characterized in that: include: Detection nodes: There are multiple detection nodes, which are distributed on the surface of Europa or under the ice layer. A federated learning model is set in each detection node to detect ice layer data in the area, train the model through a federated learning algorithm, and regularly send the trained model parameters to the central server. Central server: The central server is remotely connected to the detection nodes for collaborative updating based on the model parameters sent by each detection node in combination with the federated learning algorithm to obtain a global model, and is also used to obtain ice layer data of each detection node based on the global model.

2. According to claim 1, a distributed detection system for the Europa ice layer based on a federated learning algorithm is characterized in that: The detection node includes: a detection module, a training module and a communication module; The detection module is used to detect ice data in the area; The training module is used to perform model training through a federated learning algorithm based on the ice layer data obtained by the detection module; The communication module is used to periodically send the obtained model parameters to the central server.

3. The Europa ice layer distributed detection system based on the federated learning algorithm according to claim 2, characterized in that: The data detected by the detection node include ice thickness, composition, and temperature.

4. The Europa ice layer distributed detection system based on the federated learning algorithm according to claim 1, characterized in that: The central server is further used to send the global model obtained through collaborative updating to each detection node, and the detection node is further used to adjust its detection strategy and parameters according to the received global model.

5. A distributed detection method for the Europa ice layer based on a federated learning algorithm, implemented based on the detection system of claim 1, characterized in that: The following steps are involved: Step 1: Deploy multiple detection nodes on the surface of Europa or under the ice layer; each detection node is equipped with an ice layer detection unit and pre-loaded with a local computing model; Step 2: Each detection node collects ice data in its area; Step 3: Each detection node uses the collected ice data to train the model locally using the federated learning algorithm, and each detection node regularly uploads the model parameters to the central server; Step 4: The central server updates the model parameters uploaded by each detection node in combination with the federated learning algorithm to obtain a global model. The central server also obtains the ice data detection results of each detection node in each area based on the global model. Step 5: The central server sends the collaboratively updated global model to each detection node, and each detection node adjusts its detection strategy and parameter settings according to the updated global model; Step 6: Repeat steps 2 to 5 to continuously update the model parameters of each detection node and the global model of the central server, and obtain the ice layer data of each detection node according to the global model updated in real time by the central server.

6. The method for distributed detection of the Europa ice layer based on a federated learning algorithm according to claim 5, characterized in that: In step 2, the ice layer data collected by each detection node includes: ice layer thickness, composition, and temperature.

7. The method for distributed detection of the Europa ice layer based on a federated learning algorithm according to claim 5, characterized in that: The step 2 specifically includes the following steps: after the system is initialized, each detection node starts to work independently to collect ice layer data in the area.

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