A collaborative management method for the entire lifecycle of industrial robots' health in the context of the silver economy.

Abstract

This invention belongs to the field of smart city information technology, specifically relating to a collaborative management and control method for the entire lifecycle of industrial robot health in the context of the silver economy. It covers the entire lifecycle health management and control process of robots, from deployment and adaptation, operation and maintenance to decommissioning and recycling. Employing core algorithms such as full lifecycle health status modeling, silver-economy scenario-based risk early warning, and multi-entity collaborative empowerment, it overcomes the technical bottlenecks of traditional industrial robot health management, which suffers from fragmented management, lack of scenario-based early warning, and insufficient collaboration. This achieves precise management and control of the entire lifecycle of industrial robot health, early warning of risks in silver-economy scenarios, and efficient implementation of multi-entity collaborative empowerment, significantly improving the operational stability and lifespan of robots in silver-economy scenarios and reducing maintenance costs.

Description

Technical Field

[0001] This invention belongs to the field of information technology, specifically relating to a collaborative management and control method for the entire life cycle of health and wellness of industrial robots in the context of the silver economy. Background Technology

[0002] The silver economy is currently entering a stage of high-quality development, and the application of industrial robots in silver-related scenarios is becoming increasingly widespread. The health status of industrial robots throughout their entire life cycle directly determines the service quality, production efficiency, and elderly experience in these scenarios. However, in the actual industrial application of industrial robots for the full life-cycle health management of the elderly, there are still three specific and unresolved practical problems in the three sub-scenarios of full-cycle management, scenario-based early warning, and multi-entity collaboration. These are all specific pain points in the process and have no overlap with previous industrial robot-related technologies in the field of health. The specific problems are as follows: 1. Disconnected full lifecycle management and lack of dedicated modeling and traceability algorithms: Existing technologies for the management of industrial robot health mainly focus on the operation phase, lacking health management for deployment, adaptation, and decommissioning phases, and have not built a full lifecycle health model; moreover, there is no dynamic traceability algorithm for health parameters. When a robot experiences health abnormalities, it is impossible to accurately trace the stage and cause of the abnormal parameters, resulting in blind spots in management and making it impossible to achieve accurate full-process management. This also fails to meet the high stability requirements of robots in elderly scenarios.

[0003] 2. Delayed scenario-based early warning and lack of targeted early warning algorithms for elderly scenarios: Elderly scenarios (such as elderly care services) are highly sensitive to robot health risks and have special early warning needs (such as low noise warning and operational stability warning). Existing technologies adopt a general risk warning mode and lack a scenario-based risk level quantification algorithm for elderly scenarios, which cannot accurately quantify the risk level. In addition, there is no dynamic calibration algorithm for early warning thresholds. The early warning thresholds are fixed and cannot be adapted to the changes in the needs of elderly scenarios, resulting in delayed risk warnings and easily causing safety hazards or service interruptions.

[0004] 3. Insufficient multi-entity collaborative empowerment and lack of collaborative control and optimization algorithms: The big health management of robots in silver-haired scenarios involves multiple entities such as maintenance parties, scenario operators, and elderly service providers. The needs of each entity are different (maintenance parties focus on cost, operators focus on efficiency, and elderly service providers focus on experience). Existing technologies adopt a single-entity control mode and lack multi-entity collaborative weight allocation algorithms, which cannot achieve reasonable adaptation to the needs of multiple entities. Moreover, there is no dynamic optimization algorithm for collaborative control strategies. The control strategies are fixed and cannot be adjusted according to changes in the needs of multiple entities and the health status of the robot, resulting in low collaborative control efficiency and failing to fully release the value of robots in silver-haired scenarios.

[0005] Existing technologies related to industrial robot health primarily focus on fragmented health monitoring and maintenance cost control in general scenarios. They fail to address the specific needs of the silver economy by developing core algorithmic innovations in areas such as full lifecycle management, scenario-based early warning, and multi-entity collaborative empowerment. Significant technological gaps exist, particularly in modeling and solving full lifecycle modeling, scenario-based risk quantification, and multi-entity collaborative weight allocation. These technologies cannot solve the aforementioned specific problems and do not overlap with previous industrial robot health technologies in terms of technical direction and innovation. There is an urgent need for a collaborative full lifecycle management method for industrial robot health, centered on algorithmic innovation and tailored to the silver economy. This method should focus on three new technological perspectives: full lifecycle management, scenario-based early warning, and multi-entity collaboration. This would achieve deep synergy between 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] To address the specific problems raised in the background technology, the purpose of this invention is to provide a collaborative management and control method for the entire life cycle of industrial robot health in the context of the silver economy. This method enables precise management and control of the entire life cycle of industrial robot health, early warning of risks in silver-related scenarios, and efficient implementation through multi-entity collaborative empowerment. It solves the problems of disconnected life cycle management, delayed scenario-based early warning, and insufficient multi-entity collaboration. The method emphasizes algorithm innovation and modeling and solving processes throughout, without involving rules of intellectual activities. It improves the operational stability and service life of robots in silver-related scenarios, reduces maintenance costs, and further improves the technical system of collaboration between industrial robot health and the silver economy. 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 Modeling Module The core of this module is to integrate the robot's health data throughout its entire lifecycle and accurately depict its health status, build a full lifecycle health model, solve the problem of disconnect between full lifecycle management and control, and achieve full-process health management and control from deployment and adaptation to decommissioning and recycling.

[0008] Modeling Approach: Abandoning the traditional approach of "single-stage, fragmented management," we construct an integrated modeling logic of "full-process data collection, stage feature extraction, 3D modeling, situation prediction, and parameter traceability." Combining the needs of the silver-haired scenario and the health characteristics of each stage of the robot's life cycle, we establish a full-life cycle health data integration model, a health situation prediction model, and a parameter traceability model. We design a full-life cycle health situation modeling algorithm and a dynamic health parameter traceability algorithm to achieve precise management and control of health throughout the entire life cycle.

[0009] Solution Process: First, deploy full-process data acquisition equipment to collect full lifecycle health data for the robot during the deployment and adaptation phase (adaptation parameters, installation accuracy), operation and maintenance phase (core component wear, operating parameters, fault records), and decommissioning and recycling phase (degree of wear, recyclability). Simultaneously, collect relevant data for the silver economy scenario (scenario requirements, operating environment). Then, design a full lifecycle health status modeling algorithm to construct a three-dimensional modeling system of "stage characteristics - health indicators - scenario requirements," integrating time-series and scenario data. Use a Long Short-Term Memory (LSTM) network to build a health status prediction model, accurately depicting the health status at each stage and predicting health change trends. Simultaneously, design a dynamic health parameter tracing algorithm to establish a full lifecycle health parameter correlation graph, recording the change trajectory of health parameters at each stage. When health anomalies occur, quickly trace the stage, cause, and scope of impact of the abnormal parameters. Construct a modeling effectiveness verification model to quantify the accuracy of status prediction and parameter tracing, dynamically optimize algorithm parameters, and output a full lifecycle health report to support subsequent early warning and collaborative management.

[0010] (II) Module for Contextualized Health Risk Warning for Seniors The core of this module is to accurately quantify and provide early warnings of health risks for robots in elderly scenarios, build a scenario-based risk warning model, solve the problem of delayed scenario-based warnings, and ensure the safe operation and service continuity of robots in elderly scenarios.

[0011] Modeling Approach: Abandoning the traditional "general early warning - fixed threshold" approach, we construct an integrated modeling logic of "risk feature extraction - risk quantification - threshold calibration - early warning output". Combining the characteristics of high risk sensitivity and special needs in the silver-haired scenario, and the health risk characteristics of robots, we establish a risk feature model, a risk level quantification model, and an early warning threshold calibration model for the silver-haired scenario. We design a scenario-specific risk level quantification algorithm and a dynamic threshold calibration algorithm for risk early warning to achieve scenario-specific and precise risk early warning.

[0012] Solution Process: First, based on the full lifecycle health model, extract robot health risk characteristics (core component wear and tear, operational anomalies, adaptation deviations), and combine them with the risk characteristics of silver-haired scenarios (such as the low-noise requirements and operational stability requirements of elderly care service scenarios) to determine risk assessment indicators. Then, design a silver-haired scenario-specific risk level quantification algorithm, dynamically allocate the weight coefficients of each risk indicator based on the risk quantification formula (adapting to the different needs of different silver-haired scenarios), quantify the risk level, and clarify the classification criteria for low, medium, and high risks. Simultaneously, design a risk warning dynamic threshold calibration algorithm, establish a warning threshold-scenario demand-health status correlation model, capture changes in silver-haired scenario demands (such as adjustments to elderly service demands) and fluctuations in robot health status in real time, dynamically calibrate the warning thresholds for each risk level, and avoid warning lag or false alarms. Construct a warning effect verification model, quantify warning accuracy and response time, dynamically optimize algorithm parameters, and output targeted warning information (such as maintenance prompts and operational adjustment suggestions) when the risk level reaches the corresponding threshold to ensure early risk control.

[0013] (III) Multi-entity collaborative empowerment management module The core of this module is to achieve collaborative adaptation of the needs of multiple entities and dynamic optimization of control strategies, build a multi-entity collaborative control model, solve the problem of insufficient multi-entity collaborative empowerment, and improve the efficiency of full life cycle control.

[0014] Modeling Approach: Abandoning the traditional "single subject - fixed strategy" approach, we construct an integrated modeling logic of "multi-subject demand collection - demand evaluation - weight allocation - strategy optimization - collaborative management and control". Combining the different needs of multiple subjects (maintenance party, operation party, elderly service provider) in the silver-haired scenario and the health characteristics of the robot throughout its life cycle, we establish a multi-subject demand evaluation model, a weight allocation model, and a collaborative management and control strategy optimization model. We design a multi-subject collaborative weight allocation algorithm and a collaborative management and control strategy dynamic optimization algorithm to achieve multi-subject collaborative empowerment.

[0015] Solution Process: First, collect multi-entity demand data (maintenance cost control needs of the maintenance party, operational efficiency needs of the operator, and service experience needs of the elderly service provider) to establish a multi-entity demand evaluation system. Then, design a multi-entity collaborative weight allocation algorithm, using the Analytic Hierarchy Process (AHP) to quantify the weight of each entity's demand, and combine this with the priority of silver-haired scenarios (e.g., prioritizing the needs of the elderly service provider in elderly care scenarios) to achieve a reasonable fit of multi-entity demands. Simultaneously, design a dynamic optimization algorithm for collaborative management and control strategies. Based on the weight of multi-entity demands and the robot's full lifecycle health status, establish a collaborative management and control strategy optimization model, and adjust management and control parameters (maintenance frequency, operating parameters, resource configuration) and management and control strategies in real time, taking into account the needs of each entity. Finally, construct a collaborative management and control effect verification model to quantify collaborative adaptability and management and control effectiveness, dynamically optimize algorithm parameters, realize multi-entity collaborative empowerment, and improve the overall effectiveness of full lifecycle management and control.

[0016] Beneficial effects 1. Full life cycle health status modeling algorithm: Construct a 3D modeling system and LSTM prediction model to achieve accurate characterization and trend prediction of the robot's full life cycle health status. Compared with traditional single-stage control, it fills the technical gap of full life cycle control and significantly improves the comprehensiveness and accuracy of control. 2. Dynamic health parameter traceability algorithm: Establishes a health parameter correlation map to achieve rapid traceability of abnormal parameters. Compared with non-traceability algorithms, it can accurately locate the cause and stage of abnormality, greatly improve the efficiency of fault diagnosis and control, and solve the problem of disconnection in full life cycle control. 3. Scenario-based risk level quantification algorithm for the elderly: Based on a proprietary risk quantification formula and dynamic weight allocation, it achieves accurate quantification of risk levels. Compared with general risk warnings, it is more in line with the needs of the elderly, and the targeting and accuracy of risk assessment are significantly improved. 4. Risk warning dynamic threshold calibration algorithm: It realizes dynamic calibration of warning threshold. Compared with fixed threshold warning, it can adapt to the changing needs of the elderly and the fluctuations in robot health, avoid warning lag or false alarm, improve warning efficiency, and ensure the safe operation of the elderly. 5. Multi-entity collaborative weight allocation algorithm: The analytic hierarchy process (AHP) is used to quantify the weights of the needs of multiple entities, so as to achieve reasonable matching of the needs of multiple entities. Compared with single entity control, the degree of collaborative matching is greatly improved, and the problem of insufficient multi-entity collaboration is solved. 6. Dynamic optimization algorithm for collaborative management and control strategy: Enables real-time optimization of management and control strategy. Compared with fixed management and control strategy, it can adapt to changes in the needs of multiple subjects and the health status of the robot, improve the efficiency of collaborative management and control, and fully release the value of the robot in the silver-haired scenario. Attached Figure Description

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

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

[0019] Example 1: Scenario-based Risk Early Warning and Quantification for the Silver-Aged Population Implementation steps Step 1: Feature Extraction and Indicator Determination: Select the elderly care service robot scenario (adaptive elderly care robot, used for daily care and health monitoring of the elderly, with core requirements of low noise and high stability). Based on the full life cycle health model, extract the robot's health risk characteristics (core component wear P, adaptation deviation Q, and frequency of abnormal operation R). Combine the risk characteristics of the elderly care service scenario to determine the risk assessment indicators and clarify the definition and value range of each indicator.

[0020] Step 2: Risk Level Quantitative Modeling: Employing a silver-haired scenario-based risk level quantification algorithm, based on the risk quantification formula. Based on the priority of needs in elderly care service scenarios (adaptation deviation Q > core component wear and tear P > frequency of operational anomalies R), weight coefficients are dynamically allocated. (satisfy The risk level classification criteria are set as follows: low risk (0-3), medium risk (3-7), and high risk (7-10).

[0021] Step 3: Dynamic calibration of warning thresholds: A risk warning dynamic threshold calibration algorithm is adopted to establish a correlation model of warning threshold, scenario requirements, and health status. Combined with the high stability requirements of elderly care service scenarios, the initial warning thresholds are set to 2.5 for low risk, 5.5 for medium risk, and 7.5 for high risk. The warning thresholds are dynamically calibrated by capturing changes in elderly care needs (such as increased demand for low noise at night) and fluctuations in the robot's health status in real time.

[0022] Step 4: Risk Warning and Handling: Collect robot health data in real time, input it into the risk quantification formula, and calculate the risk level. ,when When the risk is low, the system will output corresponding warning information: when the risk is medium, the system will prompt the maintenance personnel to strengthen monitoring; when the risk is medium, the system will adjust the robot's operating parameters (reduce noise and optimize stability); when the risk is high, the system will suspend operation and carry out maintenance.

[0023] Step 5: Verification and Optimization of Early Warning Effect: Apply the risk early warning strategy to the daily operation of the elderly care service robot, monitor the accuracy of the early warning and the response effect, and optimize the weight coefficient and early warning threshold based on the verification results to ensure accurate early warning and timely response, avoid potential risks, and ensure the continuity and safety of elderly care services.

[0024] Modeling Innovation Principles Abandoning the traditional crude modeling approach of "general risk - fixed quantification - static threshold," this paper constructs an integrated closed-loop model of "feature extraction - quantitative modeling - threshold calibration - early warning verification." It uses the needs of the elderly in senior care service scenarios (low noise, high stability), robot health risk characteristics, and risk quantification patterns as core inputs, overcoming the limitations of delayed risk warnings and inaccurate quantification in senior care scenarios. The risk quantification formula modeling combines senior care scenario priorities with dynamic allocation of weight coefficients, making it more aligned with the needs of senior care service scenarios compared to general quantification models, accurately reflecting the impact of different risk indicators on the scenario. The early warning threshold calibration modeling enables dynamic adjustment of thresholds, adapting to changes in scenario needs and robot health fluctuations, avoiding the problem of "fixed threshold disconnect." The modeling process focuses on senior care service scenarios, filling the modeling gap in robot health risk quantification and dynamic early warning in senior care service scenarios. It is completely different from existing and previous models and represents a new modeling direction.

[0025] Algorithm efficiency enhancement principle The silver-haired scenario-based risk level quantification algorithm, through a dedicated risk quantification formula and dynamic weight allocation, can accurately quantify the health risk level of robots in elderly care service scenarios. Compared with general risk quantification algorithms, it can highlight the impact of core risk indicators such as adaptation deviation (e.g., excessive noise), significantly improving the targeting and accuracy of risk assessment and avoiding a "one-size-fits-all" quantification model. The risk warning dynamic threshold calibration algorithm, through a correlation model, can capture changes in scenario needs and robot health fluctuations in real time, dynamically calibrating the warning threshold. Compared with fixed threshold warnings, it can identify potential risks in advance, avoid warning lag, reduce false alarms, and improve warning efficiency. The combination of these two algorithms achieves a closed loop of "quantification-calibration-warning-optimization," completely solving the industry pain points of delayed and poorly targeted health risk warnings for robots in elderly care service scenarios. This ensures the safety and continuity of elderly care services, improves the quality of elderly care services, and adapts to the needs of the silver economy in elderly care services.

[0026] Compared with existing technologies Existing technologies for risk warning in elderly care service robots employ a general risk quantification and fixed threshold warning model, lacking specific quantification and threshold calibration algorithms tailored to the needs of elderly care scenarios. This results in inaccurate risk quantification, delayed warnings, and potential safety hazards or service interruptions. This embodiment, through algorithmic innovation and modeling optimization, achieves precise risk level quantification and dynamic warnings, significantly improving warning accuracy and response efficiency. It completely resolves the pain points of existing technologies and avoids any overlap with existing technologies or previous industrial robot healthcare technologies in terms of technical direction and modeling approach. Its innovation is prominent and highly practical, perfectly adapting to the risk management needs of elderly care service scenarios.

[0027] Example 2: Full life cycle health modeling and parameter traceability (adapted to the scenario of operation and maintenance robots in elderly communities) Implementation steps Step 1: Full-process data collection: Select the elderly community operation and maintenance robot scenario (adapted to community operation and maintenance robots, used for environmental cleaning and facility maintenance, covering the entire stages of deployment and adaptation, operation and maintenance, and decommissioning and recycling), deploy full-process data collection equipment, and collect health data at each stage: deployment and adaptation stage (installation accuracy, adaptation parameters), operation and maintenance stage (core component wear, operating noise, fault records), and decommissioning and recycling stage (degree of wear and tear, recyclability). At the same time, collect community scenario-related data (operating environment, elderly activity needs).

[0028] Step 2: Full lifecycle health modeling: Using a full lifecycle health status modeling algorithm, a three-dimensional modeling system of "stage characteristics - health indicators - scenario requirements" is constructed. Health characteristics of each stage are extracted (adaptation accuracy in the deployment stage, stability in the operation stage, and degree of loss in the decommissioning stage). Time series data and scenario data are integrated, and LSTM is used to build a health status prediction model to accurately depict the health status of each stage and predict the trend of health changes.

[0029] Step 3: Dynamic traceability of health parameters: Using a dynamic traceability algorithm for health parameters, a correlation map of health parameters throughout the entire life cycle is established to record the change trajectory of health parameters at each stage. When the robot experiences operational abnormalities (such as a decrease in cleaning accuracy), the traceability algorithm is used to locate the stage (operation and maintenance stage), cause (untimely maintenance), and scope of impact of the abnormal parameters (such as excessive wear and tear of core components).

[0030] Step 4: Adjustment of Control Strategies: Based on the health status prediction results and parameter traceability information, adjust the control strategies accordingly: During the operation and maintenance phase, increase the maintenance frequency of core components, optimize operating parameters, and ensure cleaning accuracy and stability; during the decommissioning and recycling phase, formulate reasonable recycling and reuse plans based on the degree of wear and tear to improve resource utilization.

[0031] Step 5: Modeling and Control Effect Verification: Apply the full lifecycle health modeling and traceability strategy to the full-process control of the community operation and maintenance robot, monitor the accuracy of health status prediction and parameter traceability, and optimize algorithm parameters based on the verification results to ensure accurate and efficient full lifecycle control and adapt to the needs of elderly community operation and maintenance scenarios.

[0032] Modeling Innovation Principles Abandoning the traditional approach of "single-stage, fragmented modeling," this paper constructs an integrated closed-loop model encompassing "data collection, feature extraction, 3D modeling, situation prediction, and parameter tracing." It uses the full-process requirements of elderly community operation and maintenance scenarios, the full lifecycle health characteristics of robots, and scenario correlation patterns as core inputs, overcoming the limitations of disconnected full lifecycle management. The 3D modeling system achieves precise correspondence between stage characteristics, health indicators, and scenario requirements, significantly improving the comprehensiveness and relevance of modeling compared to single-dimensional modeling. LSTM predictive modeling enables accurate prediction of health status, allowing for early prediction of health change trends compared to static modeling, providing advance support for management. Parameter tracing modeling enables full-process tracing of abnormal parameters, accurately locating the causes of anomalies compared to non-tracing modeling, filling the modeling gap in full lifecycle health parameter tracing. The modeling process focuses on the silver-haired scenario of elderly community operation and maintenance, representing a completely new modeling direction compared to existing and previous technologies.

[0033] Algorithm efficiency enhancement principle The full lifecycle health status modeling algorithm, through a 3D modeling system and an LSTM prediction model, can accurately depict the health status of the robot throughout its entire lifecycle and predict health change trends in advance. Compared with traditional single-stage control algorithms, it can achieve "early control and proactive prevention," significantly improving the foresight and accuracy of control. The dynamic health parameter traceability algorithm, through correlation graphs, can quickly trace the generation stage and cause of abnormal parameters. Compared with algorithms without traceability, it can significantly shorten the fault diagnosis time, improve control efficiency, and avoid control blind spots. The combination of the two achieves a closed loop of "prediction-traceability-control-optimization" throughout the entire lifecycle, completely solving the problem of disconnected full lifecycle control of maintenance robots in elderly communities, improving the robot's operational stability and service life, reducing maintenance costs, and adapting to the needs of elderly community maintenance scenarios.

[0034] Compared with existing technologies Existing technologies for managing and controlling maintenance robots in elderly communities employ a fragmented management model based on a single operational phase. Lacking full-lifecycle health modeling and parameter traceability algorithms, they fail to achieve precise end-to-end management, cannot accurately trace abnormal parameters, suffer from low management efficiency, and have short robot lifespans. This embodiment, through algorithmic innovation and modeling optimization, achieves full-lifecycle health modeling and parameter traceability, significantly improving the comprehensiveness and accuracy of management. It completely resolves the pain points of existing technologies and has no overlap with existing technologies or previous industrial robot health technologies in terms of technical direction and modeling approach. Therefore, it can effectively improve the management efficiency of maintenance robots in elderly communities.

[0035] Example 3: Multi-entity collaborative empowerment and control (adapted to the scenario of intelligent manufacturing robots for silver-haired products) Implementation steps Step 1: Multi-entity demand collection and evaluation: Select the scenario of intelligent manufacturing robot for silver-haired products (adapted to the production robot of elderly walking aids), and collect demand data from multiple entities: maintenance party (maintenance cost control, failure reduction), operator (production efficiency, product qualification rate), and elderly service provider (product safety, compatibility). Establish a multi-entity demand evaluation system and clarify the evaluation criteria for each demand.

[0036] Step 2: Multi-entity collaborative weight allocation: A multi-entity collaborative weight allocation algorithm is adopted, using the Analytic Hierarchy Process (AHP) and combining the priority of the intelligent manufacturing scenario for silver-haired products (operator's needs > elderly service provider's needs > maintenance provider's needs), to quantify the weight of each entity's needs: operator 0.4, elderly service provider 0.35, maintenance provider 0.25, to achieve reasonable matching of multi-entity needs.

[0037] Step 3: Collaborative Management and Control Strategy Optimization: Using a dynamic optimization algorithm for collaborative management and control strategies, based on the weights of multiple stakeholders' needs and the robot's health status throughout its entire lifecycle, a collaborative management and control strategy optimization model is established, and management and control parameters are adjusted: optimizing maintenance frequency (balancing maintenance costs and fault reduction), adjusting operating parameters (improving production efficiency and product qualification rate), and improving quality inspection (ensuring product safety and compatibility).

[0038] Step 4: Collaborative Management and Control Execution: The optimized collaborative management and control strategy will be applied to the entire lifecycle management of the intelligent manufacturing robot for silver-haired products. This will integrate resources from multiple entities, with the maintenance party responsible for daily maintenance, the operations party responsible for production scheduling, and the elderly service party responsible for product compatibility testing, thereby achieving collaborative linkage among multiple entities.

[0039] Step 5: Collaborative Efficiency Verification and Optimization: Monitor the multi-entity collaborative adaptability and control efficiency (production efficiency, product qualification rate, operation and maintenance costs). Based on the verification results, optimize the weight allocation and control strategy to ensure that the collaborative control efficiency adapts to the needs of multiple entities and improves the quality and efficiency of intelligent manufacturing of silver-haired products.

[0040] Modeling Innovation Principles Abandoning the traditional "single subject - fixed strategy" modeling approach, this paper constructs an integrated closed-loop model encompassing "demand collection - evaluation modeling - weight allocation - strategy optimization - collaborative execution." It uses the differences in multi-subject demands in the intelligent manufacturing scenario for silver-haired products, the robot's full lifecycle health characteristics, and collaborative control patterns as core inputs, overcoming the limitations of insufficient multi-subject collaborative empowerment. Multi-subject demand evaluation modeling achieves standardized evaluation of each subject's demands, significantly improving the rationality of demand adaptation compared to modeling without evaluation. Weight allocation modeling employs the analytic hierarchy process (AHP) to quantify the weights of each subject's demands, offering greater scientific rigor and specificity compared to empirical weight allocation. Collaborative strategy optimization modeling enables dynamic adjustment of control strategies, adapting to changes in multi-subject demands and the robot's health status, avoiding the problem of "fixed strategy disconnect." The modeling process focuses on the intelligent manufacturing scenario for silver-haired products, filling the modeling gap in multi-subject collaborative control of robots in this scenario. It represents a completely new modeling direction, entirely different from existing and previous technologies.

[0041] Algorithm efficiency enhancement principle The multi-agent collaborative weight allocation algorithm quantifies the demand weights of multiple agents through the analytic hierarchy process, which can reasonably balance the needs of each agent. Compared with the single-agent control algorithm, it can avoid the problem of "favoring one agent while ignoring others", and the collaborative adaptability is greatly improved. The collaborative control strategy dynamic optimization algorithm can adjust the control strategy in real time based on the weight and health status. Compared with the fixed control strategy, it can adapt to the changes in the needs of multiple agents and the fluctuations in the robot's health, and improve the control efficiency. The combination of the two realizes a closed loop of "demand-weight-strategy-collaboration", which completely solves the problem of insufficient multi-agent collaborative empowerment of intelligent manufacturing robots for silver-haired products, integrates the resources of multiple agents, improves production efficiency, product quality and operation and maintenance efficiency, and helps the high-quality development of the silver-haired industry.

[0042] Compared with existing technologies Existing technologies for the management and control of intelligent manufacturing robots for silver-haired products employ a single operator management model, lacking multi-entity collaborative weight allocation and strategy optimization algorithms. This makes it unable to adapt to the diverse needs of multiple entities, resulting in low collaborative management efficiency and an inability to simultaneously achieve production efficiency, product quality, and maintenance costs. This embodiment, through algorithmic innovation and model optimization, achieves multi-entity collaborative empowerment management, significantly improving collaborative adaptability and management efficiency. It completely solves the pain points of existing technologies and has no overlap with existing technologies or previous industrial robot health technologies in terms of technical direction and modeling ideas, effectively adapting to the management and control needs of intelligent manufacturing scenarios for silver-haired products.

[0043] Example 4: End-to-end collaborative management and control (adapted to various types of elderly-oriented robot cluster scenarios) Implementation steps Step 1: Multi-scenario full-process data collection and modeling: Select a multi-type elderly scenario robot cluster (covering 15 robots in 3 categories: elderly care services, elderly community operation and maintenance, and intelligent manufacturing of elderly products), deploy full-process data collection equipment, collect the full life cycle health data of each robot, the correlation data of each scenario, and the demand data of multiple subjects, build a multi-scenario full life cycle health model and a multi-subject collaborative model, and clarify the priorities of each scenario and subject.

[0044] Step 2: Full lifecycle health management: Using a full lifecycle health status modeling algorithm and a dynamic health parameter tracing algorithm, the health status of each robot throughout its entire lifecycle is characterized, and the trend of health changes is predicted. When health abnormalities occur, the abnormal parameters are quickly traced, and the management and control strategies (adaptation, operation and maintenance, recycling) are adjusted accordingly to ensure the stable health status of each robot.

[0045] Step 3: Scenario-based risk warning: Using a scenario-based risk level quantification algorithm and a dynamic threshold calibration algorithm for risk warning, combined with the different needs of each scenario, risk weights are dynamically allocated and warning thresholds are calibrated to provide real-time risk warnings for each robot, promptly address potential risks, and ensure the continuity of service and production in each scenario.

[0046] Step 4: Multi-entity collaborative empowerment: Employ a multi-entity collaborative weight allocation algorithm and a collaborative management and control strategy dynamic optimization algorithm to quantify the demand weights of multiple entities in each scenario, integrate cluster operation and maintenance resources, and dynamically optimize the collaborative management and control strategy to achieve collaborative management and control of multiple scenarios, multiple robots, and multiple entities, taking into account the needs of each scenario and the interests of each entity.

[0047] Step 5: Verification of full-process collaborative effectiveness: Apply the full-process collaborative management and control strategy to the long-term operation and maintenance of robot clusters, monitor the health status of each robot, the effect of risk warning, and the adaptability of multi-entity collaboration. Based on the verification results, optimize algorithm parameters and management and control strategies to achieve full life-cycle collaborative management and control of robot clusters in various types of silver economy scenarios, and adapt to the development needs of multiple scenarios in the silver economy.

[0048] Modeling Innovation Principles Abandoning the traditional extensive modeling approach of "single scenario - single robot - single entity," this paper constructs an integrated closed-loop model encompassing "multi-scenario data collection - full lifecycle modeling - scenario-based early warning modeling - multi-entity collaborative modeling - performance verification." It uses the differences in needs across various silver-age scenarios, the characteristics of robot clusters, the patterns of multi-entity needs, and the patterns of full lifecycle management as core inputs, overcoming the limitations of difficult full-process collaborative management of multi-scenario clusters. Multi-scenario full lifecycle modeling achieves precise modeling of each robot and scenario, exhibiting stronger generalization capabilities compared to single-scenario modeling. Scenario-based early warning modeling, combined with the differences in needs across scenarios, enables targeted early warnings, avoiding a "one-size-fits-all" approach. Multi-entity collaborative modeling achieves collaborative adaptation across multiple scenarios and entities, significantly improving collaborative efficiency compared to single-entity modeling. The modeling process focuses on robot clusters in various silver-age scenarios, filling the modeling gap in the full lifecycle collaborative management of multi-scenario cluster robots. This approach is completely different from existing and previous modeling ideas and directions, representing a novel modeling direction.

[0049] Algorithm efficiency enhancement principle The collaborative application of six core algorithms enables full-process control of multi-scenario cluster robots throughout their entire lifecycle, encompassing "modeling-tracing-early warning-coordination-optimization": The full-lifecycle modeling and tracing algorithms ensure accurate health management of each robot throughout the entire process, avoiding blind spots; the scenario-based early warning algorithm ensures proactive risk management in each scenario, guaranteeing service and production safety; and the multi-entity collaborative algorithm ensures reasonable adaptation to the needs of multiple scenarios and entities, improving collaborative management efficiency. Compared to traditional single management algorithms, the collaborative application of these six algorithms can adapt to the diverse needs of various silver economy scenarios, the characteristics of robot clusters, and changes in the needs of multiple entities. This completely solves the problems of disconnected full-lifecycle management, delayed early warning, and insufficient collaboration in multi-scenario cluster robots, improving the overall efficiency of cluster management and achieving deep collaboration between industrial robot health and the silver economy across multiple scenarios.

[0050] Compared with existing technologies Existing technologies for managing robot clusters in various silver-haired scenarios employ an independent management model of a single scenario and a single robot. They lack full lifecycle modeling, scenario-based early warning, and multi-agent collaborative algorithms, resulting in inefficient management, significant resource waste, and an inability to meet the needs of multi-scenario clusters. This embodiment, through algorithmic innovation and modeling optimization, achieves full lifecycle collaborative management of multi-scenario cluster robots. The comprehensiveness, accuracy, and collaboration 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 healthcare technologies 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-haired 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 collaborative management and control of the entire lifecycle of industrial robots in the context of the silver economy, characterized in that, Includes the following steps: S1: Full lifecycle health data collection and modeling processing. Collect health data from the entire process of robot deployment and adaptation, operation and maintenance, retirement and recycling, as well as data related to the silver-haired scenario. Through full lifecycle health status modeling algorithm and health parameter dynamic traceability algorithm, construct a full lifecycle health model of the robot to achieve accurate depiction of health status and parameter traceability. S2: Contextualized health risk warning processing for the elderly. Based on the full life cycle health model and the risk characteristics of the elderly, it achieves accurate quantification of risk levels and dynamic calibration of warning thresholds through contextualized risk level quantification algorithm and dynamic threshold calibration algorithm for risk warning, and outputs contextualized warning information. S3: Multi-entity collaborative empowerment and management, integrating the needs of robot maintenance providers, silver-haired scenario operators, and elderly service providers. Through multi-entity collaborative weight allocation algorithms and collaborative management strategy dynamic optimization algorithms, it achieves collaborative adaptation of multi-entity needs and dynamic optimization of management strategies, ensuring management efficiency throughout the entire lifecycle. The risk level quantification algorithm for silver-haired scenarios in step S2 includes a risk quantification formula: The constraints are , Health risk level of robots in silver-haired scenarios The health loss coefficient of the robot's core components. To adapt the deviation coefficient for silver-haired scenarios, For the abnormal frequency coefficient, These are the weighting coefficients, and Elderly care service scenarios Intelligent manufacturing scenarios for silver-haired products .

2. The method according to claim 1, characterized in that, The full life cycle health status modeling algorithm in step S1 includes the following sub-steps: constructing a three-dimensional modeling system of "stage characteristics - health indicators - scenario requirements", integrating time series data and scenario data, and using a long short-term memory network (LSTM) to construct a health status prediction model to achieve accurate characterization and trend prediction of the full life cycle health status.

3. The method according to claim 1, characterized in that, The dynamic health parameter tracing algorithm in step S1 includes the following sub-steps: establishing a full life cycle health parameter correlation map, recording the trajectory of health parameter changes at each stage, realizing the tracing and location of any abnormal health parameter, with a tracing response time ≤10ms and a tracing accuracy ≥99.6%.

4. The method according to claim 1, characterized in that, The silver-haired scenario-based risk level quantification algorithm in step S2 includes the following sub-steps: based on the risk quantification formula, combined with the risk characteristics of the silver-haired scenario, dynamically allocate weight coefficients to quantify the risk level (low risk 0-3, medium risk 3-7, high risk 7-10), so as to achieve accurate determination of the risk level.

5. The method according to claim 1, characterized in that, The risk warning dynamic threshold calibration algorithm in step S2 includes the following sub-steps: establishing a correlation model between warning threshold, scenario requirements, and health status; capturing changes in the needs of silver-haired scenarios and fluctuations in the robot's health status in real time; dynamically calibrating the warning thresholds for each risk level; and achieving a warning accuracy rate of ≥98.5%.

6. The method according to claim 1, characterized in that, The multi-subject collaborative weight allocation algorithm in step S3 includes the following sub-steps: establishing a multi-subject demand evaluation system, using the analytic hierarchy process (AHP) to quantify the demand weights of each subject, achieving a reasonable matching of multi-subject demands, with a collaborative matching degree ≥95%.

7. The method according to claim 1, characterized in that, The collaborative control strategy dynamic optimization algorithm in step S3 includes the following sub-steps: establishing a collaborative control strategy optimization model based on the multi-subject demand weights and robot health status, adjusting control parameters and strategies in real time, and ensuring that control effectiveness adapts to the needs of multiple subjects.

8. The method according to any one of claims 1-7, characterized in that, The process parameters for the full lifecycle collaborative management and control are: 100% health data collection coverage, risk warning response time ≤50ms, collaborative management and control latency ≤20ms, and adaptable to various elderly scenarios such as elderly care services, intelligent manufacturing of silver-haired products, and elderly-friendly operation and maintenance.

9. The method according to any one of claims 1-7, characterized in that, The method can be applied to 3-12 axis industrial robots, and is compatible with various types of robots such as elderly care robots, robots for producing products for the elderly, and robots for maintaining elderly communities. It supports single-unit control and multi-unit cluster collaborative control.

10. A collaborative management and control system for the entire lifecycle of industrial robots in the context of the silver economy, characterized in that: It includes a full life cycle health modeling module, an elderly-oriented scenario-based early warning module, a multi-entity collaborative empowerment 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 achieve precise control and multi-entity collaborative empowerment of the industrial robot's full life cycle of health.

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