A robot full life cycle prediction and individualized collaborative management method in a silver economy scenario

Abstract

This invention belongs to the field of smart city information technology, specifically relating to a method for predicting and personalized collaboratively managing the entire lifecycle of robots in the silver economy scenario. It covers the entire process of robot health prediction, personalized scenario adaptation, and lightweight collaborative management. Employing core algorithms such as health status prediction, personalized scenario matching, and lightweight collaborative scheduling, it overcomes the technical bottlenecks of traditional industrial robot health management, which relies on "passive response, uniform adaptation, and heavy-load collaboration." This enables early prediction of potential robot health risks, personalized adaptation to silver economy scenarios, and lightweight and efficient collaboration among multiple stakeholders, significantly improving the operational stability, scenario adaptation accuracy, and lightweight management level of robots in silver economy scenarios, while reducing maintenance costs and safety risks.

Description

Technical Field

[0001] This invention belongs to the field of information technology, specifically relating to a method for predicting and personalized collaboratively managing the entire lifecycle of robots in the context of the silver economy. Background Technology

[0002] With the rapid development of the silver economy, industrial robots are increasingly being used in elderly-oriented scenarios. Their ability to predict health status, their level of personalized adaptation to different scenarios, and their lightweight control directly impact service quality, operating costs, and the experience of the elderly. However, in the actual industrial application of industrial robots for health management and adaptation to elderly scenarios, there are still three specific and unresolved practical problems in the three sub-scenarios of full life-cycle prediction, personalized adaptation, and lightweight collaboration. These are all specific pain points in the process and do not overlap with previous industrial robot-related technologies in the health sector (including hazard tracing and dynamic adaptation). These are as follows: 1. Lack of full lifecycle health prediction capability and lack of dedicated prediction and accuracy calibration algorithms: Existing technologies can only passively respond to potential hazards after they occur, and cannot predict the health status of the robot throughout its entire lifecycle. They have not built a dedicated full lifecycle prediction model; moreover, they lack prediction accuracy calibration algorithms, and cannot calibrate the prediction accuracy according to the prediction requirements of different elderly scenarios. This results in the inability to prevent potential hazards in advance, which can easily lead to safety risks. In particular, it is not suitable for scenarios with extremely high requirements for hazard prediction accuracy, such as flexible intelligent manufacturing of elderly products.

[0003] 2. Lack of scenario-specific adaptation capabilities and algorithms for personalized demand analysis and parameter tuning: The needs of the elderly are significantly different (such as the different care needs of different elderly groups in home-based elderly care, and the different production needs of multiple categories in flexible intelligent manufacturing of elderly products). Existing technologies use a unified operating mode and parameters, lacking scenario-specific demand analysis algorithms, and cannot accurately analyze the personalized needs of different scenarios and groups; moreover, there are no personalized optimization algorithms for robot operating parameters, so the operating parameters cannot be adjusted according to personalized needs, resulting in poor robot adaptability and inability to meet the personalized needs of the elderly.

[0004] 3. Lack of lightweight collaborative management and control capabilities, and lack of lightweight collaborative scheduling and performance self-calibration algorithms: Silver-age scenarios (such as elderly care communities) are characterized by limited operation and maintenance resources and low management and control load. Existing technologies adopt a heavy-load collaborative management and control mode, lack lightweight collaborative scheduling algorithms, and cannot simplify collaborative processes or reduce management and control load. Furthermore, the lack of collaborative performance self-calibration algorithms makes it impossible to dynamically calibrate collaborative strategies, resulting in high collaborative management and control load and high operation and maintenance costs, which cannot meet the lightweight management and control requirements of silver-age scenarios.

[0005] Existing technologies related to industrial robot health primarily focus on hazard identification, passive maintenance, or standardized adaptation, failing to address the personalized and lightweight needs of the silver economy. They lack core algorithmic innovation in areas such as full lifecycle health prediction, personalized adaptation for silver-centric scenarios, and lightweight multi-entity collaboration. Significant technological gaps exist, particularly in full lifecycle prediction modeling, personalized demand analysis, and lightweight collaborative scheduling modeling and solution, failing to solve the aforementioned specific problems. Furthermore, these technologies do not overlap with previous industrial robot health technologies in terms of technical direction and innovation. There is an urgent need for a method for full lifecycle health prediction and personalized collaborative management of industrial robots, centered on algorithmic innovation and geared towards the silver economy. This method should focus on three new technological perspectives: full lifecycle prediction, personalized adaptation, and lightweight collaboration, to achieve deep integration of industrial robot health and the silver economy, fill the technological gaps in this field, and promote the high-quality development of both the silver economy and the industrial robot industry. Summary of the Invention

[0006] Addressing the three specific problems raised in the background technology, the purpose of this invention is to provide a method for predicting and personalized collaborative management of the entire life cycle of robots in the context of the silver economy. This method enables early prediction of health risks of industrial robots, personalized adaptation to silver-themed scenarios, and lightweight collaborative management by multiple stakeholders. It solves the problems of lack of prediction capabilities, lack of personalized adaptation, and lack of lightweight collaboration. The entire process emphasizes algorithm innovation and modeling and solving, without involving rules of intellectual activities. This improves the operational stability of robots in silver-themed scenarios, the accuracy of scenario adaptation, and the level of lightweight management, reduces operation and maintenance costs and safety risks, and further improves the technical system of industrial robot health and silver economy collaboration. Moreover, it does not overlap with previous technologies.

[0007] The present invention is implemented through the following specific technical solution: (I) Full Life Cycle Health Prediction Module The core of this module is to enable early prediction and accuracy calibration of the robot's health status throughout its entire life cycle, build a full life cycle health prediction model, solve the problem of lacking full life cycle health prediction capabilities, and realize closed-loop management of "health monitoring - feature extraction - early prediction - accuracy calibration - early prevention and control" to avoid potential problems from the source and ensure the stable operation of robots in elderly scenarios.

[0008] Modeling Approach: Abandoning the traditional "passive response, no prediction" approach, we construct an integrated modeling logic of "full lifecycle data collection - data preprocessing - health prediction - accuracy calibration - early prevention and control". Combining the differentiated requirements of prediction accuracy in the silver-haired scenario, the full lifecycle operation characteristics of the robot, the health decline pattern and the characteristics of potential hazards, we establish a full lifecycle data association model, a health prediction model, and an accuracy calibration model. We design a health status prediction algorithm and a prediction accuracy calibration algorithm to achieve early prediction and accuracy calibration of potential health hazards.

[0009] Solution Process: First, deploy full-dimensional, full-lifecycle data acquisition equipment to collect robot lifecycle operational data (core component wear data, runtime sequence parameters, fault history data) and health-related data (potential hazard warning features, environmental impact data, and maintenance record data). Perform noise reduction, completion, normalization, and time-series alignment preprocessing on the data. Then, design a health status prediction algorithm that integrates full-lifecycle time-series operational data and health-related data. Use a GRU combined with an attention mechanism to construct a prediction model, extracting health degradation features and potential hazard warning features (such as abnormal component wear rates and current fluctuation precursors) to make predictions. The timing, type, and severity of potential hazards are considered in conjunction with the prediction accuracy calculation formula to calculate the prediction accuracy. Simultaneously, a prediction accuracy calibration algorithm is designed to establish a dynamic prediction error calibration system. Based on the prediction requirements of different senior citizen scenarios (such as the scenario with the highest prediction accuracy requirements for flexible intelligent manufacturing of senior citizen products), the weight parameters and thresholds of the prediction model are dynamically adjusted to calibrate the prediction accuracy and ensure it meets the scenario requirements. A prediction effect verification model is constructed to quantify prediction accuracy and timeliness, dynamically optimize algorithm parameters, and output health prediction results and accuracy reports, providing early prevention and control support for subsequent personalized adaptation and lightweight collaborative management.

[0010] (II) Personalized Adaptation Module for Silver-haired Scenarios The core of this module is to accurately match the robot's operating parameters with the personalized needs of elderly scenarios, build a scenario-specific adaptation model, solve the problem of lack of scenario-specific adaptation capabilities, improve the robot's adaptation accuracy in elderly scenarios, meet the personalized needs of different scenarios and different elderly groups, and improve service and production efficiency.

[0011] Modeling Approach: Abandoning the traditional approach of "uniform model without personalized adaptation", we construct an integrated modeling logic of "personalized demand collection - demand analysis - parameter tuning - adaptation verification". Combining the personalized demand differences in silver-haired scenarios, the experience needs of the elderly, and the adjustability of robot operating parameters, we establish a personalized demand collection model, a demand analysis model, and a parameter tuning model. We design scenario-specific personalized demand analysis algorithms and robot operating parameter personalized tuning algorithms to achieve accurate personalized adaptation of scenarios.

[0012] Solution Process: First, based on health prediction results, the impact of potential health risks on personalized adaptation is eliminated, and personalized demand data for silver-haired scenarios (scenario type, key needs, priorities, and preferences of the elderly group) is collected. Then, a scenario-based personalized demand analysis algorithm is designed, using an improved K-means clustering algorithm to extract scenario-based personalized demand features, distinguishing the differences in needs between different scenarios and different elderly groups (such as the difference in care needs between elderly people living at home and semi-independent elderly people), accurately analyzing the core key points and priorities of personalized needs, and avoiding deviations in demand analysis. At the same time, a personalized optimization algorithm for robot operating parameters is designed, establishing a personalized demand-operating parameter correlation model. The analyzed personalized needs are used as input to automatically optimize robot operating parameters (operating speed, operational accuracy, energy consumption threshold), such as slowing down the operating speed and improving operational accuracy for elderly care scenarios. Finally, an adaptation effect verification model is constructed to quantify the degree of personalized adaptation and demand satisfaction rate, dynamically optimizing algorithm parameters to ensure that the adaptation accuracy meets the standards, improving the experience and productivity of silver-haired scenarios.

[0013] (III) Lightweight Multi-Entity Collaborative Management and Control Module This module's core functionality enables lightweight collaboration among multiple stakeholders for robot health, scenario adaptation, and operation and maintenance management. It constructs a lightweight collaborative management and control model to address the lack of lightweight collaborative management and control capabilities, simplifying collaborative processes, reducing management and control load, improving management and control efficiency and response speed, adapting to the lightweight management and control needs of elderly scenarios, and reducing operation and maintenance costs.

[0014] Modeling Approach: Abandoning the traditional approach of "heavy load and complex collaboration", we construct an integrated modeling logic of "lightweight data processing - collaborative scheduling - performance self-calibration - feedback iteration". Combining the lightweight management and control requirements of the silver economy scenario, the limited operation and maintenance resources, and the multi-entity management and control requirements, we establish a lightweight data processing model, a lightweight collaborative architecture model, and a collaborative performance self-calibration model. We design lightweight collaborative scheduling algorithms and collaborative performance self-calibration algorithms to achieve lightweight collaborative management and control of multiple entities.

[0015] Solution Process: First, collect health prediction data, personalized adaptation data, and operation and maintenance management data (lightweight operation and maintenance terminal status, scene management node requirements, and management task priorities). Perform lightweight data processing (data compression and redundancy removal) to reduce data transmission and processing load. Then, design a lightweight collaborative scheduling algorithm to construct a lightweight collaborative architecture of "robot-lightweight operation and maintenance terminal-scene management node." Employ edge computing technology to simplify the collaborative scheduling process and dynamically allocate lightweight management tasks (robot responsible for health monitoring and personalized adaptation, lightweight operation and maintenance terminal responsible for simple hazard handling, and scene management node responsible for...). (Responsibilities and requirements are coordinated) to achieve efficient collaboration among multiple entities and reduce the workload of collaborative management; at the same time, a collaborative efficiency self-calibration algorithm is designed, and a lightweight collaborative efficiency evaluation model is established to quantify core indicators such as prediction accuracy, personalized adaptability, and collaborative response speed, and dynamically self-calibrate collaborative management strategies (such as adjusting collaborative scheduling frequency and optimizing task allocation logic) to ensure optimal collaborative efficiency and the lowest management load; a collaborative management effect verification model is constructed to quantify management efficiency, operation and maintenance costs, and management load, and dynamically optimize algorithm parameters to achieve lightweight multi-entity collaboration in robot health, scenario adaptation, and operation and maintenance management, adapting to the needs of silver-haired scenarios.

[0016] Beneficial effects 1. Health status prediction algorithm: The prediction model is constructed by combining GRU with attention mechanism and integrating multi-dimensional data of the robot's entire life cycle to realize the early prediction of health risks. Compared with traditional passive response technology, it can prevent and control potential risks in advance, avoid safety risks, and fill the technical gap of full life cycle health prediction of robots in silver-haired scenarios. 2. Prediction accuracy calibration algorithm: Based on the prediction accuracy calculation formula and the needs of different scenarios, the prediction accuracy is dynamically calibrated. Compared with fixed accuracy prediction, it can adapt to the prediction requirements of different silver hair scenarios, with higher prediction accuracy and more targeted prediction, improving the practicality and reliability of health prediction. 3. Personalized Requirement Analysis Algorithm for Different Scenarios: An improved K-means clustering algorithm is used to extract personalized requirement features, which can accurately analyze the differences in requirements of different scenarios and different elderly groups. Compared with traditional unified requirement identification, requirement analysis is more accurate and more in line with personalized requirements, providing precise support for personalized adaptation. 4. Personalized optimization algorithm for robot operating parameters: Establish a correlation model between personalized needs and operating parameters to achieve automatic optimization of operating parameters. Compared with traditional fixed parameter operation, the accuracy of scene adaptation is greatly improved, which can meet the personalized needs of silver-haired scenarios and improve service and production efficiency. 5. Lightweight collaborative scheduling algorithm: Construct a lightweight collaborative architecture, adopt edge computing to simplify the collaborative process, and realize lightweight collaborative linkage of multiple entities. Compared with traditional heavy-load collaboration, the management and control load is greatly reduced and the management and control efficiency is significantly improved, which is suitable for the limited operation and maintenance resources in the silver-haired scenario. 6. Collaborative Performance Self-Calibration Algorithm: A lightweight collaborative performance evaluation model is established, and the collaborative strategy is dynamically self-calibrated. Compared with the traditional fixed collaborative strategy, it can achieve optimal collaborative performance and the lowest management and control load, further reducing operation and maintenance costs and improving the scientificity and efficiency of lightweight collaborative management and control. Attached Figure Description

[0017] Figure 1 : Workflow diagram of the whole life cycle health prediction module Detailed Implementation

[0018] The following four specific embodiments illustrate the implementation steps of the present invention in detail.

[0019] Example 1: Full Life Cycle Health Prediction and Accuracy Calibration Implementation steps Step 1: Full Lifecycle Data Collection: Select the scenario of flexible intelligent manufacturing robots for silver-haired products (adapted to flexible production robots such as smart wearable devices for the elderly and lightweight walking aids. The core requirement is early prediction of health hazards with high accuracy to avoid the impact of hazards on production continuity and product precision). Deploy full-dimensional, full lifecycle data collection equipment to collect robot full lifecycle operation data (wear rate of core components, operating current, vibration frequency) and health-related data (signs of potential hazards, environmental temperature and humidity, and maintenance records).

[0020] Step 2: Health Prediction Modeling: A health status prediction algorithm is adopted, based on the prediction accuracy calculation formula. Combined with the accuracy requirements of flexible intelligent manufacturing scenarios for silver-haired products ( By integrating preprocessed full lifecycle data, a predictive model is constructed using GRU combined with an attention mechanism to extract health degradation characteristics and early warning characteristics of potential hazards (such as abnormal wear rate of precision components and early current fluctuations) and predict the timing, type, and severity of potential hazards.

[0021] Step 3: Predictive Accuracy Calibration: Using a predictive accuracy calibration algorithm, a dynamic calibration system for predictive error is established to calculate the current predictive accuracy. ,like The weight parameters and thresholds of the prediction model are dynamically adjusted (increasing the weight of early warning features and optimizing the prediction thresholds) to calibrate the prediction accuracy and ensure that the prediction accuracy meets the needs of the scenario, thereby enabling early prediction of health hazards.

[0022] Step 4: Early Prevention and Feedback: Based on the health prediction results, take early preventive measures against the predicted hidden dangers (such as excessive component wear and abnormal current), replace easily worn components, adjust operating parameters, and collect data on the effectiveness of prevention and control. Feedback is then sent to the prediction model to further optimize algorithm parameters and improve the accuracy and timeliness of prediction.

[0023] Step 5: Prediction Effect Verification and Optimization: Apply the full life cycle prediction and accuracy calibration strategy to the production of flexible intelligent manufacturing robots for silver-haired products, monitor the prediction accuracy and the rate of early prevention of hidden dangers, and optimize the prediction model parameters and accuracy calibration algorithm based on the verification results to ensure that the prediction effect continues to meet the standards and avoid hidden dangers from affecting production continuity and product accuracy.

[0024] Modeling Innovation Principles Abandoning the traditional, crude modeling approach of "passive response and no prediction," this paper constructs an integrated closed-loop model encompassing "full lifecycle data collection - preprocessing - prediction - accuracy calibration - early prevention." It uses the high prediction accuracy requirements of flexible intelligent manufacturing scenarios for senior citizen products, the robot's full lifecycle operational characteristics, health decline patterns, and early warning signs as core inputs, overcoming the limitations of lacking full lifecycle prediction and inability to calibrate accuracy. Predictive modeling employs a GRU combined with an attention mechanism, integrating multi-dimensional data from the entire lifecycle. Compared to traditional static predictive modeling, it can accurately capture health decline trends and early warning signs, enabling early prediction of potential hazards. Accuracy calibration modeling is based on a dedicated calculation formula, dynamically adjusting model parameters and thresholds according to scenario-specific prediction requirements. Compared to fixed-accuracy modeling, it can adapt to the high requirements of flexible intelligent manufacturing scenarios, making prediction accuracy more controllable and reliable. The modeling process focuses on flexible intelligent manufacturing scenarios for senior citizen products, filling the modeling gap in full lifecycle health prediction and accuracy calibration for flexible intelligent manufacturing robots for senior citizen products. It is completely different from existing and previous modeling approaches and directions, representing a new modeling direction.

[0025] Algorithm efficiency enhancement principle The health status prediction algorithm, through GRU combined with an attention mechanism, can accurately extract the health decline characteristics and early warning signs of potential problems throughout the robot's entire life cycle, enabling early prediction of potential problems. Compared with traditional passive response algorithms, it can prevent potential problems from escalating and affecting production continuity and product precision, thus improving the robot's operational stability. The prediction accuracy calibration algorithm, through a dedicated calculation formula and dynamic parameter calibration, can control the prediction accuracy within the range required by the scenario. Compared with traditional fixed-precision prediction algorithms, it has higher prediction accuracy and is more targeted, adapting to the core needs of flexible intelligent manufacturing scenarios. The combination of the two achieves a closed loop of "prediction-accuracy calibration-early prevention-feedback", completely solving the industry pain points of insufficient accuracy and lack of full life cycle prediction in flexible intelligent manufacturing robots for silver-haired products, improving production continuity and product precision, helping the high-quality development of the flexible intelligent manufacturing industry for silver-haired products, and adapting to the needs of intelligent manufacturing scenarios in the silver economy.

[0026] Compared with existing technologies Existing technologies in the management of flexible intelligent manufacturing robots for senior citizen products can only respond passively after a potential hazard occurs. They lack algorithms for full lifecycle prediction and accuracy calibration, making it impossible to prevent hazards in advance. This can easily lead to production interruptions and substandard product precision, failing to meet the high requirements of flexible intelligent manufacturing scenarios. This embodiment, through algorithmic innovation and model optimization, achieves full lifecycle health hazard prediction and accuracy calibration, preventing hazards from impacting production at the source. The prediction accuracy meets the scenario requirements, completely solving the pain points of existing technologies. Furthermore, it has no overlap with existing technologies or previous industrial robot health technologies (including versions prior to this modification) in terms of technical direction and modeling approach. Its innovations are prominent and highly practical, perfectly adapting to the management needs of flexible intelligent manufacturing scenarios for senior citizen products.

[0027] Example 2: Personalized adaptation for silver-haired scenarios (adaptation to home-based elderly care assistive robot scenarios) Implementation steps Step 1: Personalized Needs and Robot Data Collection: Select a home-based elderly care assistive robot scenario (adapting home-based elderly care assistive robots for daily care and rehabilitation assistance for different elderly groups, with the core need being personalized adaptation, such as the difference in care needs between the very elderly and the semi-self-reliant elderly), collect personalized need data for the scenario (elderly group type, key care needs, priority, and operation preferences), and simultaneously input the robot's full life cycle health prediction results to ensure that the robot has no health risks.

[0028] Step 2: Personalized Needs Analysis: A scenario-based personalized needs analysis algorithm is adopted, using an improved K-means clustering algorithm to extract scenario-based personalized needs features, distinguishing the differences in needs among different elderly groups (e.g., the needs of very old people are slow-speed, high-precision care, while the needs of semi-independent elderly people are convenient operation and efficient response), accurately analyzing the core points and priorities of personalized needs, and avoiding bias in needs analysis.

[0029] Step 3: Personalized Optimization of Operating Parameters: Using a personalized optimization algorithm for robot operating parameters, a personalized needs-operating parameter correlation model is established. The analyzed personalized needs are used as input to automatically optimize the robot's operating parameters: for elderly people, the operating speed is slowed down, the operation accuracy is improved, and the noise is reduced; for semi-independent elderly people, the operation process is optimized, the response speed is improved, and the operation interface is simplified to achieve personalized and precise adaptation.

[0030] Step 4: Adaptation effect verification: Monitor the personalization adaptation degree in real time (demand fulfillment rate, elderly group satisfaction), compare the personalized needs with the robot's operating effect, and if the adaptation does not meet the standard, immediately optimize the algorithm parameters (adjust the demand clustering threshold, optimize the parameter tuning logic), adjust the operating parameters, and ensure that the adaptation effect continues to meet the standard.

[0031] Step 5: Adaptation Strategy Optimization: Apply personalized adaptation strategies to the daily operation of the home-based elderly care assistive robot, monitor the adaptation effect for different elderly groups, and optimize the scenario-specific demand parsing algorithm and parameter tuning algorithm based on the verification results to improve the accuracy and targeting of the adaptation and meet the personalized needs of different elderly groups.

[0032] Modeling Innovation Principles Abandoning the traditional modeling approach of "uniform model without personalized adaptation," this paper constructs an integrated closed-loop model of "personalized needs collection - analysis - parameter optimization - adaptation verification." It takes the personalized needs differences in home-based elderly care scenarios, the experience needs of the elderly, and the adjustability of robot operating parameters as core inputs, overcoming the limitations of lacking personalized adaptation. The needs analysis modeling uses an improved K-means clustering algorithm, which can accurately distinguish the needs differences of different elderly groups. Compared with traditional unified needs identification modeling, the needs analysis is more accurate and better suited to personalized needs. The parameter optimization modeling establishes a correlation model between personalized needs and operating parameters, achieving automatic parameter optimization. Compared with traditional fixed parameter modeling, it can adapt to the needs differences of different elderly groups, resulting in stronger adaptability. The modeling process focuses on home-based elderly care assistance scenarios, filling the modeling gap for personalized adaptation of home-based elderly care assistance robots. It is completely different from the modeling approaches and technical directions of existing and previous technologies, representing a completely new modeling direction.

[0033] Algorithm efficiency enhancement principle The scenario-based personalized needs analysis algorithm, through an improved K-means clustering algorithm, can accurately analyze the differences and priorities of personalized needs among different elderly groups. Compared with traditional unified needs identification algorithms, the accuracy of needs analysis is greatly improved, avoiding mismatches caused by biased needs analysis and enhancing the experience for the elderly. The robot operating parameter personalized optimization algorithm, through an association model, achieves automatic optimization of operating parameters. Compared with traditional fixed parameter algorithms, the accuracy of scenario adaptation is significantly improved, which can meet the personalized needs of different elderly groups, ensuring service quality and improving the user experience for the elderly. The combination of the two achieves a closed loop of "analysis-adjustment-verification-optimization", completely solving the problem of lack of personalized adaptation of home-based elderly care assistive robots, improving service efficiency and elderly satisfaction, and adapting to the needs of home-based elderly care scenarios in the silver economy.

[0034] Compared with existing technologies Existing technologies for managing home-based elderly care assistive robots employ a uniform operating mode and parameters, lacking personalized needs analysis and parameter optimization algorithms. This fails to adapt to the individualized needs of different elderly groups, resulting in poor service experiences (e.g., elderly people find the speed too fast, while semi-independent elderly find the operation cumbersome), and thus cannot meet the personalized needs of home-based elderly care. This embodiment, through algorithmic innovation and model optimization, achieves personalized scenario adaptation and automatic optimization of operating parameters, significantly improving adaptation accuracy and targeting, completely resolving the pain points of existing technologies. Furthermore, it does not overlap with existing technologies or previous industrial robot big health technologies (including versions prior to this modification) in terms of technical direction and modeling ideas, effectively improving the service efficiency of home-based elderly care assistive robots.

[0035] Example 3: Lightweight Multi-Agent Collaborative Management and Control (Adapted for Elderly Care Community Service Robot Scenarios) Implementation steps Step 1: Multi-dimensional data collection and lightweight processing: Select the scenario of elderly care community service robots (adapted to community care service robots for elderly care patrols, rehabilitation assistance, and emergency calls; core requirements are lightweight collaborative management, low management load, and timely response, involving multiple entities such as robots, lightweight operation and maintenance terminals, and community management nodes). Collect full lifecycle prediction data, personalized adaptation data, and operation and maintenance management data (lightweight operation and maintenance terminal status, community management node requirements, and management task priorities). Perform lightweight processing on the data (data compression and redundancy removal) to reduce the data transmission and processing load.

[0036] Step 2: Lightweight Collaborative Scheduling: A lightweight collaborative scheduling algorithm is adopted to construct a lightweight collaborative architecture of "robot-lightweight operation and maintenance terminal-community management node". Edge computing technology is used to simplify the collaborative scheduling process and dynamically allocate lightweight management and control tasks: the robot is responsible for health monitoring, personalized adaptation and emergency response, the lightweight operation and maintenance terminal is responsible for simple hidden danger handling, and the community management node is responsible for demand coordination and resource scheduling, so as to realize efficient collaborative linkage of multiple subjects and reduce the collaborative management and control load.

[0037] Step 3: Collaborative Performance Self-Calibration: A lightweight collaborative performance evaluation model is established using a collaborative performance self-calibration algorithm. This model quantifies core indicators such as prediction accuracy, personalized adaptability, and collaborative response speed, and dynamically self-calibrates collaborative management strategies (such as adjusting collaborative scheduling frequency and optimizing task allocation logic) to ensure optimal collaborative performance and the lowest management load.

[0038] Step 4: Lightweight Collaborative Management and Control Execution: Apply the lightweight collaborative management and control strategy to the operation of community health and wellness service robots, monitor the execution of management tasks by each entity in real time, ensure that the robot accurately monitors health, adapts to scenarios, and responds to emergencies, that the lightweight operation and maintenance terminal promptly handles simple hidden dangers, and that community management nodes efficiently coordinate resources to achieve lightweight collaborative linkage among multiple entities.

[0039] Step 5: Collaborative Effect Verification and Optimization: The lightweight multi-agent collaborative management and control strategy is applied to the long-term operation of service robots in elderly care communities. The management and control efficiency, management and control load, and operation and maintenance costs are monitored. Based on the verification results, the lightweight collaborative scheduling algorithm and the efficiency self-calibration algorithm are optimized to improve the efficiency and lightweight level of collaborative management and control, and to adapt to the management and control needs of elderly care communities.

[0040] Modeling Innovation Principles Abandoning the traditional modeling approach of "heavy load and complex collaboration," this paper constructs an integrated closed-loop model of "lightweight data processing - collaborative scheduling - performance self-calibration - feedback iteration." It takes the lightweight management needs of elderly care communities, the limited operational resources, the multi-entity management needs, and the safety needs of the elderly as core inputs, overcoming the limitations of non-lightweight collaborative management. Collaborative scheduling modeling constructs a lightweight collaborative architecture, using edge computing to simplify processes. Compared to heavy-load collaborative modeling, it can reduce management load and improve collaborative efficiency. Performance self-calibration modeling establishes a lightweight collaborative performance evaluation model, dynamically self-calibrating collaborative strategies. Compared to fixed collaborative modeling, it can achieve optimal collaborative performance and the lowest management load, aligning with the management needs of elderly care communities. The modeling process focuses on the service scenarios of elderly care communities, filling the modeling gap in lightweight multi-entity collaborative management of service robots in elderly care communities. It is completely different from existing and previous modeling approaches and directions, representing a completely new modeling direction.

[0041] Algorithm efficiency enhancement principle The lightweight collaborative scheduling algorithm simplifies the collaborative process and allocates lightweight tasks through a lightweight collaborative architecture and edge computing technology, enabling efficient multi-entity collaborative linkage. Compared with traditional heavy-load collaborative algorithms, it significantly reduces the management load and improves the collaborative response speed, making it suitable for the limited operation and maintenance resources of elderly care communities and reducing operation and maintenance costs. The collaborative efficiency self-calibration algorithm can optimize the collaborative strategy in real time through efficiency evaluation and dynamic self-calibration. Compared with traditional fixed collaborative algorithms, it has better collaborative efficiency and lower management load, further improving the scientific and efficient management. The combination of the two realizes a closed loop of "lightweight collaboration - efficiency self-calibration - verification - iteration", which completely solves the problem of lack of lightweight collaborative management of service robots in elderly care communities, improves the efficiency and lightweight level of management, ensures the safety of the elderly, and meets the needs of elderly care community scenarios in the silver economy.

[0042] Compared with existing technologies Existing technologies for managing service robots in elderly care communities employ a heavy-load collaborative management mode, lacking lightweight collaborative scheduling and efficiency self-calibration algorithms. This results in complex collaborative processes, high management loads, and an inability to adapt to the limited operational resources of elderly care communities, leading to low management efficiency, high maintenance costs, and failing to meet the lightweight management requirements of these communities. This embodiment, through algorithmic innovation and model optimization, achieves lightweight collaborative management of multiple entities, significantly reducing the management load and substantially improving management efficiency. It completely resolves the pain points of existing technologies and has no overlap with existing technologies or previous industrial robot healthcare technologies (including versions prior to this modification) in terms of technical direction and modeling approach. Therefore, it effectively adapts to the management needs of elderly care community service scenarios.

[0043] Example 4: Lightweight collaborative management and control throughout the entire process (adapted to various types of elderly-oriented robot cluster scenarios) Implementation steps Step 1: Multi-scenario, multi-dimensional data collection and lightweight processing: Select a multi-type elderly care scenario robot cluster (covering 12 robots in 3 categories: home-based elderly care assistance, flexible intelligent manufacturing of elderly care products, and elderly health and wellness community services), deploy multi-dimensional data collection equipment, collect the full life cycle operation data, health-related data, scenario-specific demand data, and operation and maintenance management data of each robot, perform noise reduction, completion, and standardization preprocessing on the data, and then perform lightweight processing (data compression and redundancy removal) to build a multi-scenario data lightweight integration model.

[0044] Step 2: Full life cycle health prediction and accuracy calibration: Using health status prediction algorithm and prediction accuracy calibration algorithm, perform full life cycle health prediction and accuracy calibration for each robot. According to the prediction requirements of different scenarios (highest accuracy in intelligent manufacturing scenario, followed by health and wellness community scenario, and home scenario can be adapted), dynamically adjust the prediction model parameters and thresholds to ensure that the prediction accuracy meets the requirements of each scenario, and output health prediction results and accuracy reports.

[0045] Step 3: Personalized Adaptation for Silver-Aged Scenarios: Using a scenario-based personalized demand analysis algorithm and a robot operation parameter personalized optimization algorithm, the personalized demand characteristics of each scenario and each elderly group are extracted. Based on the differences and priorities of the demands, the operation parameters of each robot are automatically optimized to achieve personalized and accurate adaptation of the robot to the corresponding silver-aged scenarios and elderly groups, thereby improving service and production efficiency.

[0046] Step 4: Lightweight Multi-Agent Collaborative Management and Control: A lightweight collaborative scheduling algorithm and a collaborative efficiency self-calibration algorithm are adopted to build a multi-scenario, multi-agent lightweight collaborative architecture. Lightweight management and control tasks are dynamically allocated to realize the collaborative linkage of various robots, lightweight operation and maintenance terminals, and management nodes in various scenarios. The collaborative management and control strategy is dynamically self-calibrated to achieve lightweight collaboration throughout the entire process of health prediction, personalized adaptation, and operation and maintenance management and control.

[0047] Step 5: Verification of full-process collaborative efficiency: Apply the full-process lightweight collaborative management and control strategy to the long-term operation of the robot cluster, monitor the prediction accuracy, personalization adaptability, management load, and collaborative efficiency of each robot, and optimize 6 core algorithm parameters based on the verification results to achieve full-process lightweight collaborative management and control of robot clusters in multiple types of silver economy scenarios, adapting to the development needs of multiple scenarios in the silver economy.

[0048] Modeling Innovation Principles Abandoning the traditional, crude modeling approach of "single scenario - single robot - heavy load management," this paper constructs an integrated closed-loop model encompassing "lightweight integration of multi-scenario data - health prediction - personalized adaptation - lightweight collaboration - performance verification." It uses the differences in needs across various silver-haired scenarios, robot cluster characteristics, lightweight management requirements, and health decline patterns as core inputs, overcoming the limitations of lightweight collaborative management across the entire process of multi-scenario clusters. The lightweight integration modeling of multi-scenario data enables lightweight processing and integration of multi-dimensional data from different scenarios and robots, exhibiting stronger generalization capabilities and lower management load compared to single-scenario modeling. The three modeling modules—prediction, adaptation, and collaboration—work in synergy to achieve closed-loop management throughout the entire process. Compared to single-module modeling, collaborative efficiency is significantly improved, and management load remains controllable. The modeling process focuses on robot clusters in multiple silver-haired scenarios, filling the modeling gap in the entire lifecycle prediction, personalized adaptation, and lightweight collaboration of multi-scenario cluster robots. This approach is completely different from existing and previous modeling ideas and directions, representing a new modeling direction.

[0049] Algorithm efficiency enhancement principle The collaborative application of six core algorithms enables closed-loop management of multi-scenario cluster robots throughout the entire process of "prediction-precision calibration-personalized adaptation-lightweight collaboration-efficiency self-calibration": health prediction and precision calibration algorithms ensure early prevention of potential problems for each robot, reducing the occurrence of failures; scenario-specific demand analysis and parameter optimization algorithms ensure that each robot is accurately adapted to the corresponding scenario and group, improving service and production efficiency; lightweight collaborative scheduling and efficiency self-calibration algorithms ensure lightweight collaborative linkage of multiple entities, reducing management load and improving management efficiency. Compared with traditional single management algorithms, the collaboration of the six algorithms can adapt to the different needs of various types of silver economy scenarios, robot cluster characteristics and lightweight management requirements, completely solving the problems of no prediction, no personalized adaptation, and no lightweight collaboration for multi-scenario cluster robots, improving the overall efficiency and lightweight level of cluster management, and realizing the deep integration of industrial robot health and silver economy in multiple scenarios.

[0050] Compared with existing technologies Existing technologies for managing robot clusters in various silver-age scenarios employ a heavy-load, distributed management model with a single scenario and a single robot. Lacking full lifecycle prediction, personalized adaptation, and lightweight collaborative algorithms, they fail to achieve lightweight collaborative management throughout the entire process. This results in the inability to proactively prevent potential hazards, poor adaptability, and high management load, making them unsuitable for the lightweight management needs of multi-scenario clusters. This embodiment, through algorithmic innovation and modeling optimization, achieves lightweight collaborative management of multi-scenario cluster robots throughout the entire process. The accuracy, adaptability, lightweight level, and efficiency of management are significantly improved, completely resolving the pain points of existing technologies. Furthermore, it does not overlap with existing technologies or previous industrial robot health technologies (including versions prior to this modification) in terms of technical direction and modeling approach. Its innovations are clear and highly practical, effectively contributing to the collaborative development of robot clusters in various silver-age scenarios.

[0051] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the claimed invention. The scope of protection claimed by the appended claims and their equivalents is defined.

Claims

1. A method for robot life cycle prediction and personalized collaborative management in a silver economy scenario, characterized in that, Includes the following steps: S1: Full life cycle health prediction processing, collects robot full life cycle operation data and health-related data, and constructs a robot health full life cycle prediction model through health status prediction algorithm and prediction accuracy calibration algorithm to realize early prediction of health risks and prediction accuracy calibration; S2: Personalized adaptation processing for silver-haired scenarios. Based on health prediction results and personalized needs of silver-haired scenarios, a personalized adaptation model is constructed through scenario personalized needs analysis algorithm and robot operation parameter personalized optimization algorithm to achieve personalized and accurate adaptation between the robot and silver-haired scenarios. S3: Lightweight multi-agent collaborative management and control processing, integrating prediction data, adaptation data, and operation and maintenance management data, and constructing a lightweight collaborative management and control model through lightweight collaborative scheduling algorithm and collaborative efficiency self-calibration algorithm to achieve lightweight multi-agent collaboration in robot health, scenario adaptation and operation and maintenance management; The health status prediction algorithm in step S1 includes a prediction accuracy calculation formula: The constraints are , To improve prediction accuracy, To predict the probability of potential health risks, To assess the actual probability of potential hazards occurring, flexible intelligent manufacturing scenarios for senior citizen products. Elderly care community service scenarios .

2. The method according to claim 1, characterized in that, The health status prediction algorithm in step S1 includes the following sub-steps: fusing the robot's full life cycle time-series operation data and health-related data, constructing a prediction model using a gated recurrent unit (GRU) combined with an attention mechanism, extracting health decline characteristics and early warning characteristics of potential hazards, predicting the time, type and severity of potential hazards, and realizing early prediction of health hazards.

3. The method according to claim 1, characterized in that, The prediction accuracy calibration algorithm in step S1 includes the following sub-steps: establishing a dynamic calibration system for prediction error, dynamically adjusting the weight parameters and thresholds of the prediction model in combination with the differentiated requirements for prediction accuracy in silver-haired scenarios, calibrating the prediction accuracy, and ensuring that the prediction results meet the management and control requirements of different silver-haired scenarios.

4. The method according to claim 1, characterized in that, The scenario-specific demand analysis algorithm in step S2 includes the following sub-steps: using an improved K-means clustering algorithm to extract the personalized demand features of silver-haired scenarios, distinguishing the differences in demand among different scenarios and different elderly groups, accurately analyzing the core points and priorities of personalized demands, and achieving accurate identification of personalized demands.

5. The method according to claim 1, characterized in that, The personalized optimization algorithm for robot operating parameters in step S2 includes the following sub-steps: establishing a personalized demand-operating parameter correlation model, and automatically optimizing robot operating parameters (operating speed, operation accuracy, energy consumption threshold) based on the personalized demand analysis results of the scenario to adapt to the personalized needs of different scenarios and different groups.

6. The method according to claim 1, characterized in that, The lightweight collaborative scheduling algorithm in step S3 includes the following sub-steps: constructing a lightweight collaborative architecture of "robot-lightweight operation and maintenance terminal-scenario management node", using edge computing technology to simplify the collaborative scheduling process, dynamically allocating lightweight management and control tasks, realizing efficient collaborative linkage of multiple entities, and reducing the collaborative management and control load.

7. The method according to claim 1, characterized in that, The collaborative performance self-calibration algorithm in step S3 includes the following sub-steps: establishing a lightweight collaborative performance evaluation model, quantifying core indicators such as prediction accuracy, personalized adaptability, and collaborative response speed, and dynamically self-calibrating collaborative management and control strategies to ensure optimal collaborative performance and the lowest management and control load.

8. The method according to any one of claims 1-7, characterized in that, The process parameters for the whole lifecycle prediction and personalized collaborative management are: prediction accuracy. Personalized demand analysis response time ≤8ms, lightweight collaboration response time ≤15ms, adaptable to various elderly scenarios such as home-based elderly care assistance, flexible intelligent manufacturing of elderly products, and elderly health and wellness community services.

9. The method according to any one of claims 1-7, characterized in that, The method can be applied to 2-10 axis industrial robots, and is compatible with various types of robots such as home-based elderly care assistance robots, flexible production robots for silver-haired products, and health and wellness community service robots. It supports independent control of a single robot and collaborative control of multiple lightweight clusters.

10. A system for predicting and personalized collaboratively managing the entire lifecycle of industrial robots' health in the context of the silver economy, characterized in that... It includes a full life cycle health prediction module, a silver-haired scenario personalized adaptation module, a lightweight multi-subject collaborative management and control module, and a control center. The control center communicates bidirectionally with the three functional modules and executes the method described in any one of claims 1-9 to realize the full life cycle prediction of health of industrial robots, scenario personalized adaptation, and lightweight multi-subject collaborative management and control.

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