Terminal cooperation intelligent processing and optimization method for unmanned equipment
Through multi-source data aggregation, preprocessing, task decomposition and clustering, and terminal collaborative network processing methods, the problems of unmanned equipment's limited capabilities and information islands in complex tasks are solved, and intelligent collaborative operations and data processing between unmanned equipment are realized, which improves the efficiency and robustness of the system.
Patent Information
- Application Number
- CN202510387693.2
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-03-31
- Publication Date
- 2025-07-04
AI Technical Summary
Single unmanned equipment has limited capabilities and serious information islands in complex tasks, resulting in poor task execution efficiency and accuracy.
Through multi-source data aggregation, data preprocessing, task decomposition and clustering, terminal collaborative network processing and intelligent analysis and decision-making, intelligent collaborative operations and data processing between unmanned equipment are realized, advanced communication technology and distributed network architecture are adopted, and resource allocation is optimized in combination with deep reinforcement learning algorithms.
It realizes rapid response and in-depth analysis of massive data, improves data processing efficiency, reduces system delay and energy consumption costs, provides users with personalized service experience, and improves the efficiency and robustness of the overall system.
Smart Images
Figure CN120256120A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of unmanned equipment, and particularly to a terminal collaborative intelligent processing and optimization method for unmanned equipment. Background Art
[0002] With the rapid development of technology, unmanned equipment is increasingly widely used in multiple fields such as military, civilian, and scientific research. These unmanned equipment, such as unmanned aerial vehicles, unmanned vehicles, and unmanned ships, can perform various tasks in complex and changing environments by virtue of their high autonomy and flexibility. However, with the increase in the number of unmanned equipment and the improvement of task complexity, how to efficiently process the massive data generated by these equipment and achieve collaborative operations between multiple equipment has become an urgent problem to be solved.
[0003] Traditionally, the data processing of unmanned equipment mainly relies on the processing units inside a single piece of equipment. This method can be competent when dealing with small-scale data, but its limitations become apparent when the data volume surges or multiple equipment need to collaborate. First, the processing capacity of a single piece of equipment is limited and difficult to meet the requirements of large-scale data processing. Second, the information island phenomenon between equipment is serious, making it difficult to achieve efficient data sharing and collaborative processing. Finally, traditional methods often have delays and errors when dealing with tasks with high real-time requirements, affecting the execution efficiency and accuracy of tasks.
[0004] In summary, a single unmanned equipment has problems of limited capabilities, information islands, and poor execution efficiency and accuracy in complex tasks. Summary of the Invention
[0005] The purpose of the present invention is to provide a terminal collaborative intelligent processing and optimization method for unmanned equipment to solve the problems of limited capabilities, information islands, and poor execution efficiency and accuracy of a single unmanned equipment in complex tasks.
[0006] To achieve the above object, the present invention provides a terminal collaborative intelligent processing and optimization method for unmanned equipment, and the terminal collaborative intelligent processing and optimization method for unmanned equipment includes the following steps: Data collection: Obtain data through multi-source data aggregation and real-time acquisition technology; Data preprocessing: Process the obtained data through multiple steps such as data cleaning, integration, transformation, enhancement, missing value processing, and standardization to provide high-quality data input for the follow-up; Task decomposition and clustering: Decompose complex tasks into multiple subtasks, form task clusters through the K-means clustering algorithm, and analyze the characteristics of the task clusters through the deep reinforcement learning algorithm to allocate corresponding computing resources to each task cluster to achieve optimal resource allocation; Terminal collaborative network processing: Advanced communication technologies are adopted to enable communication between unmanned equipment, and a distributed network architecture and dynamic routing technology are used to support flexible connection and collaborative operations among multiple unmanned equipment; Intelligent analysis and decision-making: Deeply mine the processed data to discover patterns and trends in the data, select appropriate algorithms and model structures for model training according to business requirements, generate intelligent decision-making suggestions based on the data analysis results, and provide real-time feedback to intelligent terminals or relevant users; Result feedback and optimization: The intelligent terminal receives the decision-making suggestions sent by the cloud, adjusts and optimizes as needed, and conducts regular evaluations and optimizations.
[0007] Among them, in the step of "data collection", the convergence of multi-source data specifically means that the data comes from various sensors, networks, file imports, and internal systems of enterprises or organizations, and real-time collection specifically means using intelligent terminals to collect data in real time.
[0008] Among them, in the step of "data preprocessing", data cleaning specifically means removing outliers and duplicate values to ensure data quality; Data integration specifically means integrating data from different sources to form a dataset in a unified format; Data conversion specifically means converting the data into a format suitable for model training; Data augmentation specifically means increasing the diversity of the dataset through technical means to improve the generalization ability of the model; Missing value handling specifically means filling in the missing values in the dataset; Data standardization specifically means standardizing the data so that the data is distributed on the same scale for convenient model training.
[0009] Among them, the step of "task decomposition and clustering" specifically includes the following steps: Task decomposition: Record in detail the definition, input, output, dependencies, and test results of each subtask for convenient team collaboration and subsequent maintenance; Allocate processing nodes: According to the characteristics of the subtasks and the computing capabilities of each layer, allocate the tasks to the corresponding processing nodes; Form task clusters: Perform K-means clustering according to the types of subtasks to form task clusters; Characteristic allocation: Through a deep reinforcement learning algorithm, intelligently analyze the characteristics of the task clusters and allocate corresponding computing resources to each task cluster.
[0010] Among them, the specific content of the step of "terminal collaborative network processing" includes: Advanced communication technology: In order to achieve high-speed and reliable communication between unmanned equipment, advanced communication technologies need to be adopted; Network architecture design: The architecture of the terminal collaborative network needs to support flexible connection and collaborative operation among multiple unmanned equipment; Data processing and collaborative processing mechanism: In the terminal collaborative network, each unmanned equipment can act as a data processing node. To achieve efficient data processing and collaborative processing, a reasonable data processing mechanism and collaborative processing algorithm need to be designed.
[0011] Among them, the specific content of the step "Intelligent analysis and decision-making" includes: Data analysis: Deeply mine the processed data to discover patterns and trends in the data; Model training: Select appropriate algorithms and model structures according to business requirements, use the training set to train the model, and adjust the model parameters to improve the accuracy of prediction and classification; Decision support: Generate intelligent decision-making suggestions based on the data analysis results and feedback these suggestions to the intelligent terminal or relevant users in real time.
[0012] Among them, the specific content of the step "Result feedback and optimization" includes: Result feedback: The intelligent terminal receives the decision-making suggestions sent by the cloud and makes adjustments and optimizations as needed; System optimization: Regularly evaluate and optimize the data processing process and algorithm models to improve the processing efficiency and accuracy.
[0013] A terminal collaborative intelligent processing and optimization method for unmanned equipment according to the present invention, through refined data collection, preprocessing, intelligent task decomposition and clustering, combined with terminal collaborative efficient processing, the data processing method of the intelligent terminal realizes fast response and in-depth analysis of massive data. The terminal collaborative intelligent processing and optimization method for unmanned equipment optimizes the data processing process, realizes fast processing and intelligent analysis of data, improves the data processing efficiency, reduces the system delay and energy consumption cost, and provides a more personalized service experience for users. Adopting this technical solution, through steps such as efficient data collection, preprocessing, task decomposition and clustering, and terminal collaborative network processing, intelligent collaborative operation and data processing among unmanned equipment are realized, problems such as limited capabilities and information islands of a single unmanned equipment in complex tasks are solved, and the effectiveness and robustness of the overall system are improved. Brief description of the drawings
[0014] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.
[0015] Figure 1 It is a flowchart of the steps of the terminal collaborative intelligent processing and optimization method for unmanned equipment provided by the present invention. Detailed implementation manners
[0016] The embodiments of the present invention will be described in detail below. The examples of the embodiments are shown in the drawings, where the same or similar reference numerals denote the same or similar elements or elements with the same or similar functions from beginning to end. The embodiments described below by referring to the drawings are exemplary and are intended to explain the present invention, and should not be construed as a limitation of the present invention.
[0017] Please refer to Figure 1 , the present invention provides a terminal collaborative intelligent processing and optimization method for unmanned equipment, and the terminal collaborative intelligent processing and optimization method for unmanned equipment includes the following steps: S1. Data collection: Obtain data through multi-source data aggregation and real-time acquisition technology; S2. Data preprocessing: Process the obtained data through multiple steps including data cleaning, integration, transformation, enhancement, missing value processing, and standardization to provide high-quality data input for the subsequent steps; S3. Task decomposition and clustering: Decompose complex tasks into multiple subtasks, form task clusters through the K-means clustering algorithm, analyze the characteristics of the task clusters through the deep reinforcement learning algorithm, and allocate corresponding computing resources to each task cluster to achieve optimal resource allocation; S4. Terminal collaborative network processing: Use advanced communication technologies to achieve communication between unmanned equipment, and adopt a distributed network architecture and dynamic routing technology to support flexible connection and collaborative operation between multiple unmanned equipment; S5. Intelligent analysis and decision-making: Deeply mine the processed data to discover patterns and trends in the data, select appropriate algorithms and model structures for model training according to business requirements, generate intelligent decision-making suggestions based on the data analysis results, and feedback them to intelligent terminals or relevant users in real time; S6. Result feedback and optimization: The intelligent terminal receives the decision-making suggestions sent by the cloud, adjusts and optimizes them as needed, and conducts evaluations and optimizations regularly.
[0018] In this embodiment, through the above steps, via refined data collection, preprocessing, intelligent task decomposition and clustering, combined with efficient terminal collaboration processing, the data processing method of the intelligent terminal realizes the rapid response and in-depth analysis of massive data. The terminal collaboration intelligent processing and optimization method for unmanned equipment realizes the rapid processing and intelligent analysis of data by optimizing the data processing flow, improves the data processing efficiency, reduces the system delay and energy consumption cost, and provides a more personalized service experience for users. By adopting this technical solution, through steps such as efficient data collection, preprocessing, task decomposition and clustering, and terminal collaboration network processing, the intelligent collaborative operation and data processing among unmanned equipment are realized, the problems such as limited capabilities and information islands of a single unmanned equipment in complex tasks are solved, and the effectiveness and robustness of the overall system are improved.
[0019] Further, in the step of "data collection", the convergence of multi-source data specifically means that the data comes from various sensors, networks, file imports, and the internal systems of enterprises or organizations, and real-time collection specifically means using intelligent terminals to collect data in real time.
[0020] In this embodiment, the data can come from various sensors (such as cameras, temperature sensors, pressure sensors, etc.), networks (such as social media, news websites, e-commerce platforms, etc.), file imports (such as CSV, Excel, databases, etc.), and the internal systems of enterprises or organizations (such as ERP, CRM, etc.). Intelligent terminals (such as mobile phones, sensors, etc.) collect data in real time to ensure the timeliness and accuracy of the data.
[0021] Further, in the step of "data preprocessing", data cleaning specifically means removing outliers and duplicate values to ensure data quality (for example, identifying and removing data that does not conform to logic or exceeds the normal range through algorithms); Data integration specifically means integrating data from different sources to form a dataset in a unified format (which helps subsequent data analysis and processing); Data transformation specifically means transforming the data into a format suitable for model training (such as numericalization, labeling, etc. This step is crucial for the training of machine learning models); Data augmentation specifically means increasing the diversity of the dataset through technical means (such as rotation, scaling, cropping, etc.) to improve the generalization ability of the model; Missing value processing specifically means filling in the missing values in the dataset (which can be filled using methods such as mean, median, interpolation, etc.); Data standardization specifically means standardizing the data so that the data is distributed on the same scale for convenient model training.
[0022] Further, the step of "task decomposition and clustering" specifically includes the following steps: Task decomposition: Record in detail the definition, input, output, dependencies, and test results of each subtask to facilitate teamwork and subsequent maintenance (maintain a certain degree of flexibility when decomposing tasks to adjust the division of subtasks according to needs in subsequent stages for distributed processing in the collaborative system); Allocate processing nodes: According to the characteristics of subtasks and the computing capabilities of each layer, allocate tasks to the corresponding processing nodes (for example, tasks with high real-time requirements can be allocated to edge servers for processing, while large-scale computing tasks can be allocated to the cloud for processing); Form task clusters: Perform K-means clustering based on the types of subtasks to form task clusters; Characteristic allocation: Through a deep reinforcement learning algorithm, intelligently analyze the characteristics of task clusters and allocate corresponding computing resources to each task cluster.
[0023] Furthermore, the specific content of the step "Terminal collaborative network processing" includes: Advanced communication technologies: To achieve high-speed and reliable communication between unmanned equipment, advanced communication technologies need to be adopted (such as 5G, Wi-Fi 6, satellite communication, etc. These technologies can provide high-bandwidth and low-latency communication links to ensure real-time data and information transmission between unmanned equipment. In addition, to meet the communication requirements of unmanned equipment in complex environments, it is also necessary to consider adopting advanced communication technologies such as adaptive modulation coding, multiple access technologies, and beamforming to improve the reliability and stability of communication); Network architecture design: The architecture of the terminal collaborative network needs to support flexible connection and collaborative operation between multiple unmanned equipment (a distributed network architecture can be adopted, with each unmanned equipment as a node in the network, connected to each other through communication links. The network architecture also needs to support dynamic routing management to adapt to the position changes of unmanned equipment during task execution. Through dynamic routing technology, data and information can be efficiently and accurately transmitted to the target node); Data processing and collaborative processing mechanism: In the terminal collaborative network, each unmanned equipment can be used as a data processing node. To achieve efficient data processing and collaborative processing, a reasonable data processing mechanism and collaborative processing algorithm need to be designed (the data processing mechanism can include steps such as data preprocessing, feature extraction, and data fusion to ensure the quality and usability of data. The collaborative processing algorithm needs to consider factors such as task allocation, resource scheduling, and information sharing between unmanned equipment to achieve efficient collaborative operation).
[0024] Furthermore, the specific content of the step "Intelligent analysis and decision-making" includes: Data analysis: Deeply mine the data processed by algorithms such as big data analysis and machine learning to discover patterns and trends in the data; Model training: Select appropriate algorithms and model structures according to business requirements, use the training set to train the model, and adjust the model parameters to improve the accuracy of prediction and classification; Decision support: Generate intelligent decision-making suggestions based on the data analysis results, such as personalized recommendations, health monitoring warnings, etc., and feedback these suggestions to intelligent terminals or relevant users in real time.
[0025] Furthermore, the specific content of the step "Result feedback and optimization" includes: Result feedback: The intelligent terminal receives the decision-making suggestions sent by the cloud and adjusts and optimizes them as needed (users can also evaluate and feedback the results through the feedback mechanism to help the system continuously improve and optimize); System optimization: Regularly evaluate and optimize the data processing flow and algorithm models to improve the processing efficiency and accuracy (for example, adjust the task decomposition and allocation strategy according to new data characteristics and business requirements, optimize the model structure and parameters, etc.).
[0026] The above-disclosed is only a preferred embodiment of the present invention. Of course, the scope of the rights of the present invention cannot be limited by this. Those of ordinary skill in the art can understand all or part of the processes of implementing the above embodiments, and the equivalent changes made according to the claims of the present invention still fall within the scope covered by the invention.
Claims
1. A terminal collaborative intelligent processing and optimization method for unmanned equipment, characterized in that It includes the following steps: Data collection: Obtain data through multi-source data aggregation and real-time acquisition technologies; Data preprocessing: Process the acquired data through multiple steps including data cleaning, integration, transformation, enhancement, missing value handling, and standardization to provide high-quality data input for subsequent processes; Task decomposition and clustering: Decompose complex tasks into multiple subtasks, form task clusters through the K-means clustering algorithm, analyze the characteristics of task clusters through the deep reinforcement learning algorithm, and allocate corresponding computing resources to each task cluster to achieve optimal resource allocation; Terminal collaborative network processing: Adopt advanced communication technologies to enable communication between unmanned equipment, and adopt a distributed network architecture and dynamic routing technologies to support flexible connection and collaborative operations among multiple unmanned equipment; Intelligent analysis and decision-making: Deeply mine the processed data to discover patterns and trends in the data, select appropriate algorithms and model structures for model training according to business requirements, generate intelligent decision-making suggestions based on the data analysis results, and feedback them to intelligent terminals or relevant users in real time; Result feedback and optimization: The intelligent terminal receives the decision-making suggestions sent by the cloud, adjusts and optimizes them as needed, and conducts evaluations and optimizations regularly.
2. The terminal collaborative intelligent processing and optimization method for unmanned equipment according to claim 1, characterized in that In the step of "data collection", multi-source data aggregation specifically means that the data comes from various sensors, networks, file imports, and internal systems of enterprises or organizations, and real-time acquisition specifically means using intelligent terminals to collect data in real time.
3. The terminal collaborative intelligent processing and optimization method for unmanned equipment according to claim 2, characterized in that In the step of "data preprocessing", data cleaning specifically means removing outliers and duplicate values to ensure data quality; Data integration specifically means integrating data from different sources to form a dataset in a unified format; Data transformation specifically means transforming the data into a format suitable for model training; Data enhancement specifically means increasing the diversity of the dataset through technical means to improve the generalization ability of the model; Missing value handling specifically means filling in the missing values in the dataset; Data standardization specifically means performing standardization processing on the data so that the data is distributed on the same scale for convenient model training.
4. The terminal collaborative intelligent processing and optimization method for unmanned equipment according to claim 3, characterized in that The step of "task decomposition and clustering" specifically includes the following steps: Task decomposition: Record in detail the definition, input, output, dependencies, and test results of each subtask for easy team collaboration and subsequent maintenance; Allocate processing nodes: Allocate tasks to corresponding processing nodes according to the characteristics of the subtasks and the computing capabilities of each layer; Form task clusters: Perform K-means clustering according to the types of subtasks to form task clusters; Characteristic allocation: Through the deep reinforcement learning algorithm, intelligently analyze the characteristics of task clusters and allocate corresponding computing resources to each task cluster.
5. The terminal collaborative intelligent processing and optimization method for unmanned equipment according to claim 4, characterized in that The specific content of the step of "terminal collaborative network processing" includes: Advanced communication technology: To achieve high-speed and reliable communication between unmanned equipment, advanced communication technology needs to be adopted; Network architecture design: The architecture of the terminal cooperation network needs to support flexible connection and cooperative operation between multiple unmanned equipment; Data processing and cooperative processing mechanism: In the terminal cooperation network, each unmanned equipment can be used as a data processing node. To achieve efficient data processing and cooperative processing, a reasonable data processing mechanism and cooperative processing algorithm need to be designed.
6. The method for terminal cooperative intelligent processing and optimization for unmanned equipment according to claim 5, characterized in that, The specific content of the step "intelligent analysis and decision-making" includes: Data analysis: Deeply mine the processed data to discover patterns and trends in the data; Model training: Select appropriate algorithms and model structures according to business requirements, use the training set to train the model, and adjust the model parameters to improve the accuracy of prediction and classification; Decision support: Generate intelligent decision-making suggestions based on the data analysis results and feedback these suggestions to the intelligent terminal or relevant users in real time.
7. The method for terminal cooperative intelligent processing and optimization for unmanned equipment according to claim 6, characterized in that, The specific content of the step "result feedback and optimization" includes: Result feedback: The intelligent terminal receives the decision-making suggestions sent by the cloud and makes adjustments and optimizations as needed; System optimization: Regularly evaluate and optimize the data processing process and algorithm models to improve the processing efficiency and accuracy.