Method and system for motion path feature analysis based on data exploration

Abstract

The present application relates to the field of aerospace technology, and in particular to a motion path feature analysis method and system based on data exploration. The method comprises: obtaining multi-dimensional motion data of the aerospace equipment under different environments; the multi-dimensional motion data at least includes: the position, the motion speed, the acceleration and the motion trajectory of the aerospace equipment; extracting the motion path features of the aerospace equipment from the multi-dimensional motion data; inputting the motion path features into a target feature analysis network, and performing strategy analysis on the historical motion path of the aerospace equipment through the target feature analysis network to obtain the motion strategy features of the aerospace equipment; and generating a motion path analysis report of the aerospace equipment based on the motion strategy features, to assist in the optimization control of the aerospace equipment. The method realizes intelligent spacecraft path analysis under complex conditions, helps to optimize the motion path planning and control strategy of the aerospace equipment, and improves the operation efficiency and safety of the aerospace equipment.

Description

Technical Field

[0001] This invention relates to the field of aerospace technology, and in particular to a method and system for motion path feature analysis based on data exploration. Background Technology

[0002] In the field of aerospace technology, data exploration refers to using data analysis and mining techniques to explore and discover valuable information and patterns hidden within massive amounts of data. The aerospace field generates a vast amount of data during daily operations and research and development, including telemetry data, image data, and experimental data from satellites, rockets, spacecraft, sensors, and other equipment. Through data exploration, researchers can use methods such as data mining, statistical analysis, and machine learning to extract useful information from this data, revealing potential patterns and regularities, and helping to improve the design, execution, and evaluation processes of space missions. Data exploration has significant applications in the aerospace technology field, helping researchers better understand and utilize data, improving work efficiency and research levels.

[0003] In the context of data exploration, spacecraft path analysis enables more precise and efficient path planning and control. In related technologies, spacecraft path analysis often requires optimizing performance indicators such as fuel / energy consumption and mission time while meeting complex constraints such as mission environment and spacecraft physical performance. These path analysis problems typically involve constrained nonlinear optimal control problems. Spacecraft path analysis and control are characterized by high dimensionality of state variables, numerous constraints, strong coupling, and high uncertainty, posing significant challenges in both theoretical and technical implementation.

[0004] Therefore, there is an urgent need to design a novel spacecraft path analysis scheme to improve the accuracy of spacecraft path analysis under complex conditions. Summary of the Invention

[0005] This invention addresses the technical problems existing in the prior art by providing a motion path feature analysis method and system based on data exploration. This enables intelligent spacecraft path analysis under complex conditions, which helps optimize the motion path planning and control strategies of space equipment and improve the operational efficiency and safety of space equipment.

[0006] In a first aspect, embodiments of this application provide a motion path feature analysis method based on data exploration, the method comprising:

[0007] Acquire multidimensional motion data of aerospace equipment under different environments; the multidimensional motion data includes at least: the location of the aerospace equipment, its speed, acceleration, and trajectory;

[0008] Extract the motion path features of the aerospace equipment from the multidimensional motion data;

[0009] The motion path features are input into the target feature analysis network, and the historical motion paths of the space equipment are analyzed by the target feature analysis network to obtain the motion strategy features of the space equipment; the motion strategy features include at least the correlation features between the historical motion paths of the space equipment and the dynamic control mode adapted to the environment in which the space equipment is located.

[0010] Based on the motion strategy characteristics, a motion path analysis report for the aerospace equipment is generated to assist in the optimized control of the aerospace equipment.

[0011] Secondly, embodiments of this application provide a motion path feature analysis system based on data exploration, characterized in that the system includes the following units, wherein,

[0012] The acquisition unit is configured to acquire multidimensional motion data of aerospace equipment under different environments; the multidimensional motion data includes at least: the location of the aerospace equipment, its speed, acceleration, and trajectory.

[0013] The extraction unit is configured to extract motion path features of the spacecraft from the multidimensional motion data;

[0014] The analysis unit is configured to input the motion path features into a target feature analysis network, and perform strategy analysis on the historical motion paths of the spacecraft through the target feature analysis network to obtain the motion strategy features of the spacecraft; the motion strategy features include at least the correlation features between the historical motion paths of the spacecraft and the dynamic control modes adapted to the environment in which the spacecraft is located;

[0015] The generation unit is configured to generate a motion path analysis report for the spacecraft based on the motion strategy characteristics, in order to assist in the optimized control of the spacecraft.

[0016] Thirdly, embodiments of this application provide an electronic device, the electronic device comprising:

[0017] At least one processor, memory, and input / output unit;

[0018] The memory is used to store computer programs, and the processor is used to call the computer programs stored in the memory to execute the first aspect of the motion path feature analysis method based on data exploration.

[0019] Fourthly, a computer-readable storage medium is provided, comprising instructions that, when executed on a computer, cause the computer to perform the data exploration-based motion path feature analysis method of the first aspect.

[0020] The beneficial effects of this invention are: it provides a motion path feature analysis method and system based on data exploration. In this technical solution, firstly, multi-dimensional motion data of aerospace equipment under different environments is acquired; the multi-dimensional motion data includes at least: the location of the aerospace equipment, its velocity, acceleration, and trajectory. Then, motion path features of the aerospace equipment are extracted from the multi-dimensional motion data. Next, the motion path features are input into a target feature analysis network, which performs strategy analysis on the historical motion paths of the aerospace equipment to obtain motion strategy features; the motion strategy features include at least: the correlation features between the historical motion paths of the aerospace equipment and the dynamic control mode adapted to the environment in which the aerospace equipment is located. Finally, based on the motion strategy features, a motion path analysis report of the aerospace equipment is generated to assist in the optimized control of the aerospace equipment.

[0021] The technical solution of this application, through in-depth exploration and analysis of multi-dimensional motion data of aerospace equipment, combined with the strategy analysis of target feature analysis network, can effectively extract key motion path features, reveal the laws and patterns in historical motion paths, and realize intelligent spacecraft path analysis under complex conditions. This provides more accurate and scientific support for the dynamic control of aerospace equipment, helps to optimize the motion path planning and control strategy of aerospace equipment, and improves the operating efficiency and safety of aerospace equipment. Attached Figure Description

[0022] Figure 1 This is a flowchart illustrating a motion path feature analysis method based on data exploration according to an embodiment of this application;

[0023] Figure 2 This is a schematic diagram of the structure of a motion path feature analysis system based on data exploration according to an embodiment of this application;

[0024] Figure 3 This is a schematic diagram of the structure of an electronic device according to an embodiment of this application;

[0025] Figure 4 This is a schematic diagram of the structure of a media device according to an embodiment of this application. Detailed Implementation

[0026] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0027] In the description of this application, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the stated features. In the description of this application, "multiple" means two or more, unless otherwise explicitly specified.

[0028] In the description of this application, the term "for example" is used to mean "used as an example, illustration, or description." Any embodiment described as "for example" in this application is not necessarily to be construed as being more preferred or advantageous than other embodiments. The following description is provided to enable any person skilled in the art to make and use the invention. Details are set forth in the following description for purposes of explanation. It should be understood that those skilled in the art will recognize that the invention can be made without using these specific details. In other instances, well-known structures and processes will not be described in detail to avoid obscuring the description of the invention with unnecessary detail. Therefore, the invention is not intended to be limited to the embodiments shown, but is consistent with the broadest scope of the principles and features disclosed in this application.

[0029] In the context of data exploration, spacecraft path analysis enables more precise and efficient path planning and control. In related technologies, spacecraft path analysis often requires optimizing performance indicators such as fuel / energy consumption and mission time while meeting complex constraints such as mission environment and spacecraft physical performance. These path analysis problems typically involve constrained nonlinear optimal control problems. Spacecraft path analysis and control are characterized by high dimensionality of state variables, numerous constraints, strong coupling, and high uncertainty, posing significant challenges in both theoretical and technical implementation.

[0030] This application provides a motion path feature analysis method and system based on data exploration. In this technical solution, firstly, multi-dimensional motion data of aerospace equipment under different environments is acquired; the multi-dimensional motion data includes at least: the location of the aerospace equipment, its velocity, acceleration, and trajectory. Then, motion path features of the aerospace equipment are extracted from the multi-dimensional motion data. Next, the motion path features are input into a target feature analysis network, which performs strategy analysis on the historical motion paths of the aerospace equipment to obtain motion strategy features; the motion strategy features include at least: the correlation between the historical motion paths of the aerospace equipment and the dynamic control mode adapted to the environment in which the aerospace equipment is located. Finally, based on the motion strategy features, a motion path analysis report of the aerospace equipment is generated to assist in the optimized control of the aerospace equipment.

[0031] The technical solution of this application, through in-depth exploration and analysis of multi-dimensional motion data of aerospace equipment, combined with the strategy analysis of target feature analysis network, can effectively extract key motion path features, reveal the laws and patterns in historical motion paths, and realize intelligent spacecraft path analysis under complex conditions. This provides more accurate and scientific support for the dynamic control of aerospace equipment, helps to optimize the motion path planning and control strategy of aerospace equipment, and improves the operating efficiency and safety of aerospace equipment.

[0032] The motion path feature analysis scheme based on data exploration provided in this application embodiment can also be executed by an electronic device, such as a server, server cluster, or cloud server. This electronic device can also be a terminal device such as a mobile phone, computer, tablet computer, wearable device, or dedicated device (such as a dedicated terminal device with a motion path feature analysis system based on data exploration). These electronic devices can also incorporate the chips described in the above embodiments. Alternatively, these electronic devices can also install a service program for executing the motion path feature analysis scheme based on data exploration.

[0033] Figure 1 A flowchart illustrating a motion path feature analysis method based on data exploration, as provided in this application embodiment, is shown below. Figure 1 As shown, the method includes the following steps:

[0034] 101. Acquire multidimensional motion data of aerospace equipment under different environments;

[0035] 102. Extract the motion path features of the aerospace equipment from the multidimensional motion data;

[0036] 103. The motion path features are input into the target feature analysis network, and the historical motion paths of the space equipment are analyzed by the target feature analysis network to obtain the motion strategy features of the space equipment; the motion strategy features include at least the correlation features between the historical motion paths of the space equipment and the dynamic control mode adapted to the environment in which the space equipment is located.

[0037] 104. Based on the motion strategy characteristics, generate a motion path analysis report for the aerospace equipment to assist in the optimized control of the aerospace equipment.

[0038] In this embodiment, the multidimensional motion data includes at least: the location of the spacecraft, its velocity, acceleration, and trajectory. Specifically, in this embodiment, the multidimensional motion data includes the following key parameters: First, the location of the spacecraft: the current coordinate position information of the spacecraft, usually expressed in the form of latitude, longitude, altitude, etc. This parameter helps determine the specific location of the spacecraft and is very important for path planning and location monitoring. Second, the velocity: the velocity of the spacecraft within a specific time period, usually expressed as the distance traveled per unit time. Changes in velocity can affect the motion state and dynamic control scheme of the equipment, which is of great significance to the execution of space missions. Third, acceleration: the acceleration of the spacecraft during its motion, describing the rate and direction of velocity change. Acceleration information helps analyze the motion characteristics of the equipment in a dynamic environment, providing a more accurate basis for control and planning. Fourth, the trajectory: the trajectory path information of the spacecraft over a period of time, which can be expressed by a set of coordinate points or a curve. The trajectory reflects the actual motion path of the equipment in space and is crucial for the evaluation and optimization of the motion process. By combining the above parameters and comprehensively analyzing multi-dimensional motion data such as the location, speed, acceleration, and trajectory of aerospace equipment, we can gain a comprehensive understanding of the equipment's motion under different environments, providing a data foundation and decision-making basis for formulating motion strategies and optimizing control.

[0039] As an optional embodiment, in 101, acquiring multidimensional motion data of aerospace equipment under different environments can be achieved in the following specific ways: First, real-time location information of the aerospace equipment, including longitude, latitude, and altitude, is obtained through a GPS positioning system to achieve accurate monitoring and recording of the aerospace equipment's location. Second, inertial measurement units, such as accelerometers and gyroscopes, are used to measure the acceleration and angular velocity of the aerospace equipment in real time to reflect the equipment's motion state and changes. Sensors or monitoring devices carried by the aerospace equipment are used to record the equipment's motion trajectory in space, i.e., the coordinate trajectory data of the aerospace equipment at different points in time. Third, environmental sensors, such as weather stations, temperature sensors, and humidity sensors, are installed to monitor the meteorological and climatic information of the environment in which the aerospace equipment is located in real time, providing support for environmental characteristic analysis. Fourth, communication data is recorded. This is used to record communication data between the aerospace equipment and the ground command center, including command transmission and data feedback, to understand the equipment's operating status and interaction during the communication process.

[0040] The multidimensional motion data obtained through the above methods can comprehensively demonstrate the motion characteristics and behavior of aerospace equipment in different environments, providing an important data foundation and reference for subsequent motion path feature analysis and motion strategy optimization. This data will help to gain a deeper understanding of the motion behavior of aerospace equipment, providing support for improving equipment operation, increasing efficiency, and ensuring safety.

[0041] As an optional embodiment, in step 102, extracting the motion path features of the spacecraft from the multidimensional motion data includes the following steps:

[0042] 1021, Perform image recognition processing on the environmental image data in the multidimensional motion data to obtain the flight path length and curvature of the aerospace equipment in the current environment;

[0043] 1022, Extract real-time route information from the navigation system data of the multi-dimensional motion data; the real-time navigation information includes at least: the real-time heading, real-time speed, and real-time position of the aerospace equipment;

[0044] 1023, Extract historical navigation records from the historical navigation data of the multidimensional motion data; the historical navigation records are used to analyze the historical navigation behavior and inertial motion patterns of the space equipment;

[0045] 1024. Based on the flight path length, curvature of the flight path, real-time flight path information, and historical flight records of the aerospace equipment, motion path features of the aerospace equipment are constructed; the motion path features are used to represent the flight status and behavior characteristics of the aerospace equipment in the current environment.

[0046] In this optional embodiment, extracting the motion path features of the spacecraft from the multidimensional motion data can be performed according to the following steps:

[0047] In step 1021, image recognition processing is performed on the environmental image data in the multidimensional motion data to identify the environmental information around the spacecraft and extract features such as flight path length and curvature to understand the impact of the current environment on the flight path. In step 1022, real-time flight path information, including the spacecraft's real-time heading, real-time speed, and real-time position, is extracted from the navigation system data of the multidimensional motion data to monitor the spacecraft's motion status at the current moment. In step 1023, historical flight records of the spacecraft are extracted from the historical flight data of the multidimensional motion data, including past flight routes, behaviors, and inertial motion patterns. Historical data analysis helps to understand the spacecraft's motion behavior patterns. In step 1024, based on the spacecraft's flight path length, curvature, real-time flight path information, and historical flight records, motion path features are constructed to characterize the spacecraft's flight status and behavior characteristics in the current environment. These features can help understand the spacecraft's motion habits, environmental adaptability, and path planning methods.

[0048] By implementing the above steps, the motion path characteristics of space equipment can be effectively extracted, and the operational status and behavioral characteristics of space equipment under different environments can be comprehensively analyzed, providing important reference for subsequent motion strategy analysis and control optimization. Such analysis helps improve the path planning, operational efficiency, and safety of space equipment, thereby enhancing the execution level and success rate of space missions.

[0049] As an optional embodiment, it is assumed that the target feature analysis network includes at least the following structure: an input layer, a feature extraction layer, a policy analysis layer, and an output layer. The input layer is the first layer of the neural network structure, responsible for receiving raw input data and passing it to the next layer. In this scenario, the input layer receives motion path feature data of aerospace equipment extracted from multi-dimensional motion data, using this data as input to the neural network. The feature extraction layer is a key part of the neural network, responsible for extracting meaningful features from the input data. In this example, the feature extraction layer processes and analyzes the input motion path feature data of the aerospace equipment, learns the hidden feature information in the data, and transforms it into a higher-level representation. The policy analysis layer performs further analysis and decision-making based on the extracted feature information. In this embodiment, the policy analysis layer comprehensively considers the motion path feature data of the aerospace equipment and formulates policies based on the learned feature information to optimize the motion behavior of the aerospace equipment in different environments. The output layer is the last layer of the neural network structure, responsible for generating the final output result. In this context, the output layer transforms the decision result formulated by the policy analysis layer into highly readable information, representing the optimized motion path features and providing guidance for the motion behavior of the aerospace equipment. As described above, the structure of the entire target feature analysis network is clear and straightforward. It receives raw data from the input layer, extracts key features through the feature extraction layer, formulates decisions through the strategy analysis layer, and finally displays the optimized results through the output layer. This network structure facilitates a comprehensive analysis of the motion path characteristics of aerospace equipment, providing support for developing reasonable motion strategies and optimizing control schemes.

[0050] Based on the above structure, in step 103, the motion path features are input into the target feature analysis network, and the historical motion paths of the spacecraft are analyzed through the target feature analysis network to obtain the motion strategy features of the spacecraft, including the following steps:

[0051] 1031, through the input layer, the motion path features of the aerospace equipment are received as input features;

[0052] 1032, Through the feature extraction layer, key information features in the input features are extracted through multi-layer neural network units to identify multi-dimensional potential pattern features in the motion path features; the multi-dimensional potential pattern features include at least: path selection pattern features, action sequence pattern features, action plan pattern features, and decision basis pattern features;

[0053] 1033, Through the strategy analysis layer, the motion strategy law of the aerospace equipment is analyzed according to the multidimensional potential law characteristics to obtain the target strategy of the aerospace equipment;

[0054] 1034. Through the output layer, motion strategy features of the aerospace equipment are generated based on the target strategy; the motion strategy features include at least: path selection strategy features, action sequence strategy features, action plan strategy features, and decision basis strategy features.

[0055] In this optional embodiment, the strategy analysis of the historical motion paths of the spacecraft using a target feature analysis network to obtain the motion strategy characteristics of the spacecraft can be performed according to the following steps:

[0056] 1031. The motion path features of aerospace equipment are received as input features through the input layer, and these features are passed to the next layer for processing and analysis.

[0057] 1032, through a feature extraction layer, utilizes multi-layer neural network units to extract key information features from the input features, identifying multi-dimensional potential patterns in motion path features, such as path selection patterns, action sequence patterns, action plan patterns, and decision-making basis patterns. These patterns can help understand the behavioral patterns and decision-making basis in the historical motion paths of aerospace equipment.

[0058] 1033, through the strategy analysis layer, analyzes the motion strategy patterns of aerospace equipment based on multidimensional potential patterns and characteristics, summarizes the target strategies of aerospace equipment from historical motion paths, and helps to understand and predict the future motion behavior of aerospace equipment.

[0059] 1034. Through the output layer, motion strategy characteristics of aerospace equipment are generated based on the obtained target strategy, including path selection strategy characteristics, action sequence strategy characteristics, action plan strategy characteristics, and decision-making basis strategy characteristics. These strategy characteristics can provide guidance and reference for the future motion planning and decision-making of aerospace equipment.

[0060] For example, by using such a target feature analysis network, historical motion path data of spacecraft can be input into the network. Through deep learning, key features can be extracted to analyze the motion strategy patterns of the spacecraft. This method allows for a better understanding of the motion behavior patterns of spacecraft, predicting their future motion path selection and decision-making, and providing more intelligent and effective support for the planning and execution of space missions.

[0061] In this embodiment of the application, optionally, the network parameters of the target feature analysis network are adjusted by optimizing the loss function; wherein, the optimized loss function is expressed as the following formula:

[0062]

[0063] Where S represents the adjustment parameters of the target feature analysis network, n represents the total number of parameter samples of the target feature analysis network, and y i1 y represents the parameter to be adjusted in the target feature analysis network. i2 This represents the actual adjustment parameters of the target feature analysis network. This represents the distance between the parameter adjustments of the target feature analysis network and the variance. This represents the mean of the parameter adjustment variance distance of the target feature analysis network.

[0064] By optimizing the loss function, network parameters can be iteratively adjusted to minimize the loss function, thereby improving the performance and accuracy of the target feature analysis network. Continuous optimization of network parameters allows the network to better fit data, improve generalization ability and accuracy, and thus better analyze the motion path characteristics of aerospace equipment and formulate effective motion strategy features.

[0065] The beneficial effects of this method lie in improving the performance and accuracy of the target feature analysis network, enabling it to better adapt to the data characteristics of aerospace equipment and providing more accurate motion strategy analysis and prediction capabilities. Optimizing the loss function can help the network learn the patterns and characteristics of the data more effectively, thereby providing stronger support for the motion strategy analysis and optimization of aerospace equipment and enhancing the overall system's intelligence level.

[0066] In this embodiment of the application, optionally, in step 1033, the motion strategy law of the aerospace equipment is analyzed by the strategy analysis layer according to the multidimensional potential law characteristics to obtain the target strategy of the aerospace equipment, including the following steps;

[0067] 10331, perform cross-validation on the multidimensional potential regularity features;

[0068] 10332, Select the target strategy that matches the verification conditions from the multidimensional potential regularity features based on the cross-validation results.

[0069] In this embodiment of the application, optionally, when the strategy analysis layer analyzes the motion strategy patterns of aerospace equipment based on multidimensional latent pattern characteristics to obtain the target strategy of the aerospace equipment, in step 10331, cross-validation is performed on the multidimensional latent pattern characteristics. Cross-validation is an effective method for evaluating the generalization ability of a model. By dividing the dataset into training and validation sets, the generalization effect of the model on new data can be verified. In this step, cross-validation of multidimensional latent pattern characteristics helps ensure that the obtained target strategy has good generalization ability and applicability. In step 10332, based on the cross-validation results, a target strategy matching the validation conditions is selected from the multidimensional latent pattern characteristics. By matching the validation conditions, the target strategy that best matches the validation results can be selected. That is, by validating and analyzing the multidimensional pattern characteristics, the target strategy that best matches and is most effective for the motion strategy patterns of aerospace equipment is finally determined. Through the above steps, the motion strategy patterns of aerospace equipment can be analyzed more comprehensively and objectively. Cross-validation confirms the accuracy and reliability of the analysis results, and a matching target strategy is selected based on the validation results. This can improve the credibility and applicability of the analysis results, provide more effective and reliable guidance for the formulation of motion strategies for aerospace equipment, and thus enhance the performance and intelligence level of the entire aerospace system.

[0070] For example, in step 103, we assume a large amount of historical motion path data for spacecraft, including features such as path selection, action sequence, action plan, and decision-making basis. We input this data into a target feature analysis network, and through steps such as feature extraction and strategy analysis, we ultimately obtain the motion strategy characteristics of the spacecraft. This process allows us to understand the motion strategy patterns and decision-making basis of spacecraft under different conditions, thus providing reference and guidance for the planning and execution of future space missions.

[0071] Thus, by using target feature analysis networks to analyze historical motion paths, strategic analysis can help aerospace equipment achieve intelligent decision support. Motion strategy characteristics analyzed from historical data can provide more intelligent and precise decision guidance for aerospace equipment, improving mission execution efficiency and accuracy. By analyzing historical motion paths and identifying multi-dimensional potential patterns, it's possible to optimize the motion planning of aerospace equipment. Understanding the patterns in path selection, action sequence, action plans, and decision-making basis makes motion planning more rational and effective, improving the overall operational efficiency of aerospace equipment. Obtaining the motion strategy characteristics of aerospace equipment can better guide its motion behavior during space mission execution. Based on the analyzed target strategies, aerospace equipment can better execute missions, reduce error rates, improve execution results, and ensure successful mission completion.

[0072] This example demonstrates that analyzing the historical motion paths of spacecraft through target feature analysis networks to obtain motion strategy characteristics can bring beneficial effects such as intelligent decision support, optimized motion planning, and improved mission execution, thereby enhancing the efficiency and overall level of space missions.

[0073] As an optional embodiment, after step 103, the travel plan data of the spacecraft can also be obtained; the travel plan data includes at least: the route to be traveled by the spacecraft within a preset time period in the future; the environment in which the spacecraft will be located within the preset time period in the future is predicted based on the route to be traveled, so as to obtain the predicted environment information of the spacecraft; and the motion path adjustment strategy of the spacecraft is generated based on the predicted environment information and the motion strategy characteristics.

[0074] In this optional embodiment, by inputting the motion path characteristics of the spacecraft into a target feature analysis network for strategy analysis, and obtaining the motion strategy characteristics of the spacecraft, further travel plan data of the spacecraft can be acquired. This travel plan data includes the spacecraft's planned route within a preset future time period, predicted environmental information, and motion path adjustment strategies generated based on the predicted environmental information and motion strategy characteristics.

[0075] For example, suppose a target feature analysis network is used to perform strategy analysis on the historical motion paths of a spacecraft, obtaining the motion strategy features of the spacecraft. These features are then used to generate travel plan data for the spacecraft. In this process, a time range can be preset, the planned route of the spacecraft within this time range can be obtained, and the future environmental conditions of the spacecraft can be obtained by predicting environmental information. Finally, by combining the predicted environmental information and the motion strategy features, a motion path adjustment strategy for the spacecraft is generated.

[0076] In this way, intelligent path planning can be achieved by acquiring the travel plan data of spacecraft. Based on the motion strategy characteristics obtained from historical motion path analysis, combined with predicted environmental information, more intelligent and reasonable travel plans can be formulated for spacecraft to adapt to future environmental changes. Predicting the travel route and environmental information of spacecraft within a preset timeframe helps improve prediction accuracy. By combining predicted environmental information and motion strategy characteristics to generate motion path adjustment strategies, the future travel status of spacecraft can be predicted more accurately, improving decision-making accuracy and execution effectiveness. Generating motion path adjustment strategies for spacecraft can help optimize the motion path. Adjusting the motion path of spacecraft based on predicted environmental information and motion strategy characteristics makes it more consistent with actual conditions and mission requirements, improving the efficiency and safety of spacecraft movement. This example demonstrates that using a target feature analysis network to analyze historical motion paths and generate travel plan data for spacecraft brings beneficial effects such as intelligent path planning, improved prediction accuracy, and optimized motion path adjustments, thereby improving the execution efficiency and overall level of space missions.

[0077] As an optional embodiment, in step 104, a motion path analysis report of the aerospace equipment is generated based on the motion strategy characteristics to assist in the optimized control of the aerospace equipment.

[0078] For example, suppose we perform strategy analysis on the historical motion paths of spacecraft using a target feature analysis network to obtain the motion strategy characteristics of the spacecraft. In this example, a detailed motion path analysis report can be generated based on these characteristics. This report may include a summary of the spacecraft's motion strategies, key decision-making criteria, recommended action plans, etc. This report can assist in the optimized control of spacecraft, providing decision support and guidance.

[0079] In this embodiment, the motion path analysis report can provide relevant information and data support to help make more informed decisions. The report includes summaries of motion strategy characteristics and action plan recommendations, providing guidance to optimize the motion path control of spacecraft. By analyzing the motion path characteristics in the report, the motion strategy patterns and decision-making basis of the spacecraft can be understood, thereby optimizing motion path planning. The key information and suggestions provided in the report allow for the development of more reasonable and effective motion path plans, improving the efficiency and accuracy of spacecraft motion. Assisting in the optimized control of spacecraft can improve control efficiency. Based on the information and suggestions provided in the motion path analysis report, the motion path of spacecraft can be monitored and adjusted more accurately, ensuring it moves according to the optimized plan and improving the efficiency and accuracy of mission execution.

[0080] This example demonstrates that generating motion path analysis reports for aerospace equipment based on motion strategy characteristics can provide beneficial effects such as decision support and guidance, optimize motion path planning, and improve control efficiency, thereby enhancing the efficiency and quality of motion control for aerospace equipment.

[0081] This application provides a motion path feature analysis method based on data exploration. By deeply exploring and analyzing the multi-dimensional motion data of aerospace equipment and combining it with the strategy analysis of the target feature analysis network, it can effectively extract key motion path features, reveal the patterns and regularities in historical motion paths, and realize intelligent spacecraft path analysis under complex conditions. This provides more accurate and scientific support for the dynamic control of aerospace equipment, helps to optimize the motion path planning and control strategies of aerospace equipment, and improves the operating efficiency and safety of aerospace equipment.

[0082] In another embodiment of this application, a motion path feature analysis system based on data exploration is also provided, see [link to relevant documentation]. Figure 2 The motion path feature analysis system based on data exploration includes the following units:

[0083] The acquisition unit is configured to acquire multidimensional motion data of aerospace equipment under different environments; the multidimensional motion data includes at least: the location of the aerospace equipment, its speed, acceleration, and trajectory.

[0084] The extraction unit is configured to extract motion path features of the spacecraft from the multidimensional motion data;

[0085] The analysis unit is configured to input the motion path features into a target feature analysis network, and perform strategy analysis on the historical motion paths of the spacecraft through the target feature analysis network to obtain the motion strategy features of the spacecraft; the motion strategy features include at least the correlation features between the historical motion paths of the spacecraft and the dynamic control modes adapted to the environment in which the spacecraft is located;

[0086] The generation unit is configured to generate a motion path analysis report for the spacecraft based on the motion strategy characteristics, in order to assist in the optimized control of the spacecraft.

[0087] Further optionally, the extraction unit, which extracts the motion path features of the spacecraft from the multidimensional motion data, is configured as follows:

[0088] Image recognition processing is performed on the environmental image data in the multidimensional motion data to obtain the flight path length and curvature of the aerospace equipment in the current environment;

[0089] Real-time flight information is extracted from the navigation system data of the multi-dimensional motion data; the real-time navigation information includes at least: the real-time heading, real-time speed, and real-time position of the aerospace equipment;

[0090] Historical navigation records are extracted from the historical navigation data of the multidimensional motion data; these historical navigation records are used to analyze the historical navigation behavior and inertial motion patterns of the spacecraft.

[0091] Based on the flight path length, curvature of the flight path, real-time flight path information, and historical flight records of the aerospace equipment, motion path features of the aerospace equipment are constructed; the motion path features are used to represent the flight status and behavior characteristics of the aerospace equipment in the current environment.

[0092] Further optionally, the target feature analysis network includes at least the following structure: an input layer, a feature extraction layer, a policy analysis layer, and an output layer;

[0093] The analysis unit inputs the motion path features into the target feature analysis network, and performs strategy analysis on the historical motion paths of the spacecraft through the target feature analysis network to obtain the motion strategy features of the spacecraft, which is configured as follows:

[0094] The input layer receives the motion path features of the aerospace equipment as input features.

[0095] The feature extraction layer extracts key information features from the input features through multi-layer neural network units to identify multi-dimensional potential pattern features in the motion path features; the multi-dimensional potential pattern features include at least: path selection pattern features, action sequence pattern features, action plan pattern features, and decision basis pattern features;

[0096] Through the strategy analysis layer, the motion strategy of the aerospace equipment is analyzed based on the multidimensional potential pattern characteristics to obtain the target strategy of the aerospace equipment.

[0097] Through the output layer, motion strategy features of aerospace equipment are generated based on the target strategy; the motion strategy features include at least: path selection strategy features, action sequence strategy features, action plan strategy features, and decision basis strategy features.

[0098] Optionally, the network parameters of the target feature analysis network are adjusted by optimizing a loss function; wherein the optimized loss function is expressed as the following formula:

[0099]

[0100] Where S represents the adjustment parameters of the target feature analysis network, n represents the total number of parameter samples of the target feature analysis network, and y i1y represents the parameter to be adjusted in the target feature analysis network. i2 This represents the actual adjustment parameters of the target feature analysis network. This represents the distance between the parameter adjustments of the target feature analysis network and the variance. This represents the mean of the parameter adjustment variance distance of the target feature analysis network.

[0101] Further optionally, the analysis unit, through the strategy analysis layer, analyzes the motion strategy law of the aerospace equipment according to the multidimensional potential law characteristics to obtain the target strategy of the aerospace equipment, and is configured as follows:

[0102] Cross-validation of the aforementioned multidimensional potential regularity features;

[0103] Based on the cross-validation results, the target strategy that matches the validation conditions is selected from the multidimensional potential regularity features.

[0104] Further optionally, the analysis unit, after inputting the motion path features into a target feature analysis network and performing strategy analysis on the historical motion paths of the spacecraft through the target feature analysis network to obtain the motion strategy features of the spacecraft, is further configured to:

[0105] Acquire the travel plan data of the spacecraft; the travel plan data shall include at least the route to be taken by the spacecraft within a preset time period in the future;

[0106] Based on the proposed route, the environment in which the space equipment will be located within a preset time period in the future is predicted to obtain the predicted environmental information of the space equipment.

[0107] Based on the predicted environmental information and the motion strategy characteristics, a motion path adjustment strategy for aerospace equipment is generated.

[0108] Optionally, the analysis unit is configured to predict the environment in which the spacecraft will be located within a preset time period based on the route to be traveled, in order to obtain the predicted environmental information of the spacecraft.

[0109] Multiple path feature points are extracted from the route to be traveled to construct a path feature sequence to be analyzed;

[0110] The path feature sequence to be analyzed is input into the environmental prediction model to analyze the future environmental change patterns of aerospace equipment between various path feature points;

[0111] Based on the aforementioned future environmental change patterns, the environmental change patterns of the aerospace equipment in the environment within a predetermined time period are predicted to obtain the predicted environmental information.

[0112] The system can implement various steps in the above method embodiments, which will not be elaborated here.

[0113] In this embodiment of the application, the motion path feature analysis system based on data exploration, combined with the strategy analysis of the target feature analysis network, can effectively extract key motion path features, reveal the patterns and regularities in historical motion paths, and realize intelligent spacecraft path analysis under complex conditions. This provides more accurate and scientific support for the dynamic control of aerospace equipment, helps to optimize the motion path planning and control strategies of aerospace equipment, and improves the operating efficiency and safety of aerospace equipment.

[0114] Please see Figure 3 , Figure 3 This is a schematic diagram illustrating an embodiment of the electronic device provided in this invention. For example... Figure 3 As shown, this embodiment of the invention provides an electronic device 500, including a memory 510, a processor 5102, and a computer program 511 stored in the memory 510 and executable on the processor 5102. When the processor 5102 executes the computer program 511, it performs the following steps: acquiring multi-dimensional motion data of aerospace equipment under different environments; the multi-dimensional motion data includes at least: the location of the aerospace equipment, its speed, acceleration, and trajectory; extracting motion path features of the aerospace equipment from the multi-dimensional motion data; inputting the motion path features into a target feature analysis network, and performing strategy analysis on the historical motion paths of the aerospace equipment through the target feature analysis network to obtain motion strategy features of the aerospace equipment; the motion strategy features include at least: the correlation features between the historical motion paths of the aerospace equipment and the dynamic control mode adapted to the environment in which the aerospace equipment is located; and generating a motion path analysis report of the aerospace equipment based on the motion strategy features to assist in the optimized control of the aerospace equipment.

[0115] Please see Figure 4 , Figure 4 This is a schematic diagram illustrating an embodiment of a computer-readable storage medium provided by an embodiment of the present invention. For example... Figure 4As shown, this embodiment provides a computer-readable storage medium 600 storing a computer program 611. When executed by a processor, the computer program 611 performs the following steps: acquiring multi-dimensional motion data of aerospace equipment under different environments; the multi-dimensional motion data includes at least: the location of the aerospace equipment, its speed, acceleration, and trajectory; extracting motion path features of the aerospace equipment from the multi-dimensional motion data; inputting the motion path features into a target feature analysis network, and performing strategy analysis on the historical motion paths of the aerospace equipment through the target feature analysis network to obtain motion strategy features of the aerospace equipment; the motion strategy features include at least: the correlation features between the historical motion paths of the aerospace equipment and the dynamic control mode adapted to the environment in which the aerospace equipment is located; and generating a motion path analysis report of the aerospace equipment based on the motion strategy features to assist in the optimized control of the aerospace equipment.

[0116] It should be noted that the descriptions of each embodiment in the above embodiments have different focuses. For parts that are not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0117] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0118] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0119] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0120] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0121] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including both the preferred embodiments and all changes and modifications falling within the scope of the invention.

[0122] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A motion path feature analysis method based on data exploration, characterized in that, The method includes: Acquire multidimensional motion data of aerospace equipment under different environments; the multidimensional motion data includes at least: the location of the aerospace equipment, its speed, acceleration, and trajectory; Extract the motion path features of the aerospace equipment from the multidimensional motion data; The motion path features are input into the target feature analysis network, and the historical motion paths of the space equipment are analyzed by the target feature analysis network to obtain the motion strategy features of the space equipment; the motion strategy features include at least the correlation features between the historical motion paths of the space equipment and the dynamic control mode adapted to the environment in which the space equipment is located. Based on the motion strategy characteristics, a motion path analysis report for the aerospace equipment is generated to assist in the optimized control of the aerospace equipment; The target feature analysis network includes at least the following structure: input layer, feature extraction layer, policy analysis layer, and output layer; The step of inputting the motion path features into a target feature analysis network, and performing strategy analysis on the historical motion paths of the spacecraft through the target feature analysis network to obtain the motion strategy features of the spacecraft includes: The input layer receives the motion path features of the aerospace equipment as input features. The feature extraction layer extracts key information features from the input features through multi-layer neural network units to identify multi-dimensional potential pattern features in the motion path features; the multi-dimensional potential pattern features include at least: path selection pattern features, action sequence pattern features, action plan pattern features, and decision basis pattern features; Through the strategy analysis layer, the motion strategy of the aerospace equipment is analyzed based on the multidimensional potential pattern characteristics to obtain the target strategy of the aerospace equipment. Through the output layer, motion strategy features of aerospace equipment are generated based on the target strategy; the motion strategy features include at least: path selection strategy features, action sequence strategy features, action plan strategy features, and decision basis strategy features.

2. The motion path feature analysis method according to claim 1, characterized in that, The extraction of motion path features of aerospace equipment from the multidimensional motion data includes: Image recognition processing is performed on the environmental image data in the multidimensional motion data to obtain the flight path length and curvature of the aerospace equipment in the current environment; Real-time route information is extracted from the navigation system data of the multi-dimensional motion data; the real-time route information includes at least: the real-time heading, real-time speed, and real-time position of the aerospace equipment; Historical navigation records are extracted from the historical navigation data of the multidimensional motion data; these historical navigation records are used to analyze the historical navigation behavior and inertial motion patterns of the spacecraft. Based on the flight path length, curvature of the flight path, real-time flight path information, and historical flight records of the aerospace equipment, motion path features of the aerospace equipment are constructed; the motion path features are used to represent the flight status and behavior characteristics of the aerospace equipment in the current environment.

3. The motion path feature analysis method according to claim 2, characterized in that, The network parameters of the target feature analysis network are adjusted by optimizing the loss function; wherein, the optimized loss function is expressed as the following formula: in, This represents the adjustment parameters of the target feature analysis network. This represents the total number of parameter samples in the target feature analysis network. This represents the parameters to be adjusted for the target feature analysis network. This represents the actual adjustment parameters of the target feature analysis network. This represents the distance between the parameter adjustments of the target feature analysis network and the variance. This represents the mean of the parameter adjustment variance distance of the target feature analysis network.

4. The motion path feature analysis method according to claim 2, characterized in that, The step of analyzing the motion strategy patterns of aerospace equipment through the strategy analysis layer based on the multidimensional potential pattern characteristics to obtain the target strategy of aerospace equipment includes: Cross-validation of the aforementioned multidimensional potential regularity features; Based on the cross-validation results, the target strategy that matches the validation conditions is selected from the multidimensional potential regularity features.

5. The motion path feature analysis method according to claim 1, characterized in that, After inputting the motion path features into the target feature analysis network and performing strategy analysis on the historical motion paths of the spacecraft through the target feature analysis network to obtain the motion strategy features of the spacecraft, the process further includes: Acquire the travel plan data of the spacecraft; the travel plan data shall include at least the route to be taken by the spacecraft within a preset time period in the future; Based on the proposed route, the environment in which the space equipment will be located within a preset time period in the future is predicted to obtain the predicted environmental information of the space equipment. Based on the predicted environmental information and the motion strategy characteristics, a motion path adjustment strategy for aerospace equipment is generated.

6. The motion path feature analysis method according to claim 5, characterized in that, The prediction of the environment in which the spacecraft will be located within a preset time period based on the proposed route, to obtain the predicted environmental information of the spacecraft, includes: Multiple path feature points are extracted from the route to be traveled to construct a path feature sequence to be analyzed; The path feature sequence to be analyzed is input into the environmental prediction model to analyze the future environmental change patterns of aerospace equipment between various path feature points; Based on the aforementioned future environmental change patterns, the environmental change patterns of the aerospace equipment in the environment within a predetermined time period are predicted to obtain the predicted environmental information.

7. A motion path feature analysis system based on data exploration, characterized in that, Based on the motion path feature analysis method according to claim 1, the system includes the following units, wherein... The acquisition unit is configured to acquire multidimensional motion data of aerospace equipment under different environments; the multidimensional motion data includes at least: the location of the aerospace equipment, its speed, acceleration, and trajectory. The extraction unit is configured to extract motion path features of the spacecraft from the multidimensional motion data; The analysis unit is configured to input the motion path features into a target feature analysis network, and perform strategy analysis on the historical motion paths of the spacecraft through the target feature analysis network to obtain the motion strategy features of the spacecraft; the motion strategy features include at least the correlation features between the historical motion paths of the spacecraft and the dynamic control modes adapted to the environment in which the spacecraft is located; The generation unit is configured to generate a motion path analysis report for the spacecraft based on the motion strategy characteristics, in order to assist in the optimized control of the spacecraft.

8. An electronic device, characterized in that, include: Memory, used to store computer software programs; A processor is configured to read and execute the computer software program, thereby implementing the motion path feature analysis method based on data exploration as described in any one of claims 1-6.

9. A non-transitory computer-readable storage medium, characterized in that, The storage medium stores a computer software program, which, when executed by a processor, implements the motion path feature analysis method based on data exploration as described in any one of claims 1-6.

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