Method and device for evaluating residual life of mechanical spring

Abstract

The invention discloses a residual life evaluation method and device for a mechanical spring, and belongs to the technical field of mechanical engineering, and the method comprises the steps: obtaining basic parameter information; performing fatigue analysis based on the basic parameter information and the historical use data; based on the initial predicted residual life, introducing load fluctuation, carrying out working stress and stress concentration factor conjoint analysis, introducing environmental change, carrying out working stress and surface treatment mode conjoint analysis, introducing material weakness distribution, and carrying out stress concentration factor and surface treatment mode conjoint analysis; and connecting a nano repair robot, repairing the target mechanical spring by using a programmable material, introducing multi-source monitoring equipment, obtaining a load fluctuation sequence, an environment change sequence and a material weakness distribution map, and obtaining a residual life evaluation result under the current working condition. According to the invention, the problem that the residual life of the mechanical spring cannot be accurately evaluated and the repair parameters cannot be optimized in a complex environment condition in the prior art is solved.

Description

Technical Field

[0001] This invention relates to the field of mechanical engineering technology, specifically to a method and apparatus for assessing the remaining life of a mechanical spring. Background Technology

[0002] Mechanical springs, as components capable of withstanding external forces and exhibiting elastic deformation, are widely used in various fields such as machinery, electrical appliances, and automobiles. They play multiple crucial roles in systems, including shock absorption, energy storage, and motion control. Therefore, performance evaluation and remaining life prediction of mechanical springs are essential for ensuring normal equipment operation and improving safety.

[0003] With advancements in technology and industrial development, the performance requirements for mechanical springs are becoming increasingly stringent. Traditional evaluation methods often fail to comprehensively and accurately reflect the actual working condition and remaining life of a spring. Currently, most evaluations of mechanical springs are based on their fundamental parameters (such as material, diameter, and free length). However, while these fundamental parameters are important, they cannot fully reflect the performance changes and remaining life of a spring under actual working conditions. In particular, when a spring is subjected to complex loads, environmental changes, or material weaknesses, its performance can significantly degrade. Summary of the Invention

[0004] This application provides a method and apparatus for assessing the remaining life of mechanical springs, aiming to solve the problem in the prior art that it is impossible to accurately assess the remaining life of mechanical springs under complex environmental conditions and optimize repair parameters.

[0005] In view of the above problems, this application provides a method and apparatus for assessing the remaining life of mechanical springs.

[0006] The first aspect disclosed in this application provides a method for assessing the remaining life of a mechanical spring. This method includes obtaining basic parameter information of the target mechanical spring, wherein the basic parameter information includes material, diameter, free length, wire diameter, number of turns, pitch, spring unfolded length, and helical direction, and the diameter includes the spring's inner diameter and outer diameter; based on the basic parameter information and historical usage data of the target mechanical spring, performing fatigue analysis to assess the initial estimated remaining life of the spring under current operating conditions, wherein the historical usage data includes the number of cycles, working length, and working load; and based on the initial estimated remaining life, introducing load fluctuations corresponding to the current operating conditions, performing a joint analysis of working stress and stress concentration factor to obtain a first estimated remaining life. Based on the initial estimated remaining lifespan, environmental changes corresponding to the current working conditions are introduced, and a joint analysis of working stress and surface treatment methods is performed to obtain the second estimated remaining lifespan. Based on the initial estimated remaining lifespan, material weakness distribution corresponding to the current working conditions is introduced, and a joint analysis of stress concentration factor and surface treatment methods is performed to obtain the third estimated remaining lifespan. A nano-repair robot is connected, and programmable materials are used to repair the target mechanical spring. After the repair is completed, a multi-source monitoring device is introduced to acquire the load fluctuation sequence, environmental change sequence, and material weakness distribution map under the current working conditions. The first estimated remaining lifespan, the second estimated remaining lifespan, and the third estimated remaining lifespan are integrated to obtain the remaining lifespan assessment result under the current working conditions.

[0007] Another aspect of this application discloses a device for assessing the remaining life of a mechanical spring. This device includes a basic parameter information acquisition module for acquiring basic parameter information of the target mechanical spring, wherein the basic parameter information includes material, diameter, free length, wire diameter, number of turns, pitch, spring unfolded length, and helical direction, and the diameter includes the spring's inner diameter and outer diameter; a fatigue analysis module for performing fatigue analysis based on the basic parameter information and historical usage data of the target mechanical spring to assess the initial estimated remaining life of the spring under current operating conditions, wherein the historical usage data includes the number of cycles, working length, and working load; and a load fluctuation joint analysis module for performing joint analysis of working stress and stress concentration factor based on the initial estimated remaining life, incorporating load fluctuations corresponding to the current operating conditions, to obtain a first estimated remaining life. The system comprises three modules: a second estimated remaining lifespan and a third estimated remaining lifespan. The first module is a joint analysis module for environmental changes, based on the initial estimated remaining lifespan and incorporating environmental changes corresponding to the current working conditions. The second module is a joint analysis module for material weakness distribution, based on the initial estimated remaining lifespan and incorporating material weakness distribution corresponding to the current working conditions. The third module is a repair module, connected to a nano-repair robot, which uses programmable materials to repair the target mechanical spring. After repair, a multi-source monitoring device is introduced to acquire the load fluctuation sequence, environmental change sequence, and material weakness distribution map under the current working conditions. These are then integrated with the first, second, and third estimated remaining lifespans to obtain the remaining lifespan assessment result under the current working conditions.

[0008] One or more technical solutions provided in this application have at least the following technical effects or advantages: By employing a technical solution that involves acquiring basic parameters of mechanical springs, combining historical usage data for fatigue analysis, assessing initial remaining lifespan, and incorporating multiple joint analyses of load fluctuations, environmental changes, and material weakness distribution, and integrating various data using nano-repair robots and multi-source monitoring equipment, the problem of accurately assessing the remaining lifespan of mechanical springs under complex environmental conditions and optimizing repair parameters in existing technologies has been solved, thereby achieving the technical effect of improving the reliability and service life of mechanical springs.

[0009] The above description is only an overview of the technical solution of this application. In order to better understand the technical means of this application and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of this application more obvious and understandable, the following are specific embodiments of this application. Attached Figure Description

[0010] Figure 1This application provides a flowchart illustrating a method for assessing the remaining life of a mechanical spring.

[0011] Figure 2 This application provides a schematic diagram of the structure of a device for assessing the remaining life of a mechanical spring.

[0012] Figure labeling: Basic parameter information acquisition module 11, fatigue analysis module 12, load fluctuation joint analysis module 13, environmental change joint analysis module 14, weakness distribution joint analysis module 15, repair module 16. Detailed Implementation

[0013] The overall concept of the technical solution provided in this application is as follows: This application provides a method and apparatus for assessing the remaining life of a mechanical spring. Fatigue analysis is performed by acquiring the spring's basic parameters and historical usage data to initially assess its remaining life. Subsequently, load fluctuations, environmental changes, and material weakness distribution are introduced under current operating conditions, and multiple joint analyses are conducted to derive three predicted remaining lifespans. Finally, a nano-repair robot is used for repair, and real-time data is acquired through multi-source monitoring equipment to comprehensively assess the current remaining lifespan.

[0014] After introducing the basic principles of this application, various non-limiting embodiments of this application will be described in detail below with reference to the accompanying drawings.

[0015] Example 1 like Figure 1 As shown in the embodiment of this application, a method for assessing the remaining life of a mechanical spring is provided, the method comprising: Step S100: Obtain the basic parameter information of the target mechanical spring, wherein the basic parameter information includes material, diameter, free length, wire diameter, number of turns, pitch, spring unfolded length, and helix direction, and the diameter includes the inner diameter of the spring and the outer diameter of the spring.

[0016] Specifically, for existing mechanical springs, the most direct method is to obtain their basic parameter information directly through measuring tools. This includes using tools such as vernier calipers and micrometers to measure parameters such as the spring's diameter (including inner and outer diameters), wire diameter, and free length. If the target mechanical spring was designed and manufactured by a professional organization or manufacturer, its basic parameter information is recorded in relevant design documents or technical data. These documents include the spring's design drawings, specification sheets, and instruction manuals.

[0017] Obtaining the basic parameter information of the target mechanical spring through this step is crucial for a comprehensive understanding of its performance and characteristics. These parameters not only provide fundamental data support for the design, manufacture, and use of the spring, but also offer important insights for subsequent performance evaluation, remaining life prediction, and failure analysis.

[0018] Step S200: Based on the basic parameter information and the historical usage data of the target mechanical spring, perform fatigue analysis to evaluate the initial expected remaining life of the spring under the current working conditions. The historical usage data includes the number of cycles, working length, and working load.

[0019] Specifically, information such as the material, diameter (inner and outer diameter), free length, wire diameter, number of turns, pitch, spring unfolded length, and helix direction of the mechanical spring is obtained. These basic parameters are fundamental data for fatigue analysis, directly affecting the spring's mechanical properties and life prediction. Historical data on the spring during actual use is collected, including: cycle count: the number of loading and unloading cycles the spring has experienced; working length: the compression or extension length of the spring under working conditions; and working load: the force the spring bears under working conditions. Historical usage data reflects the stress state and deformation of the spring during actual use and is an important input for fatigue analysis. Then, by combining stress calculations and SN curves with historical usage data, the fatigue damage of the spring under current working conditions is evaluated. Specifically, stress calculation uses material mechanics and spring design theory to calculate the stress distribution of the spring under different working loads. For example, the maximum shear stress or equivalent stress of the spring is calculated using formulas. SN curve acquisition involves obtaining the material's SN curve (stress-life curve) based on the material's fatigue characteristics. This curve describes the fatigue life of the material at different stress levels. Damage accumulation analysis uses fatigue damage accumulation theories such as Miner's rule to calculate cumulative damage by substituting data such as the number of cycles and working load into the SN curve. Finally, based on the accumulated fatigue damage, the remaining life of the spring is evaluated. This includes... This step, through fatigue analysis and life assessment methods, provides a scientific basis for the maintenance and replacement of mechanical springs, ensuring the reliability and safety of the system.

[0020] Step S300: Based on the initial estimated remaining life, the load fluctuation corresponding to the current working conditions is introduced, and the working stress and stress concentration factor are jointly analyzed to obtain the first estimated remaining life.

[0021] Specifically, the initial estimated remaining life is derived from fatigue analysis based on the spring's fundamental parameters and historical usage data. Building upon this, load fluctuation data of the spring under current operating conditions is collected. This data is typically acquired in real-time by multi-source monitoring devices such as pressure sensors and acceleration sensors. Current load fluctuations reflect the dynamic stress changes experienced by the spring during actual operation and are a crucial factor affecting the spring's remaining life.

[0022] The stress distribution of the spring under different working loads is calculated based on load fluctuation data. Specific steps include: Instantaneous stress calculation: Using spring mechanics formulas and load fluctuation data, the working stress of the spring at each instant is calculated. Stress time series: The instantaneous stress data is converted into a stress time series, recording the stress changes of the spring throughout its entire working cycle. Working stress analysis can identify the maximum and average stresses experienced by the spring during operation, providing data support for subsequent stress concentration factor analysis.

[0023] Considering the local discontinuities and geometric characteristics of the spring structure, the stress concentration factor is calculated. The specific steps include: the stress concentration factor is the ratio of the maximum local stress to the nominal stress. Based on the spring's geometric characteristics (such as helix angle, wire diameter variation, etc.), the local stress in the stress concentration region is calculated using finite element analysis or empirical formulas. Stress concentration factor analysis reveals the stress amplification effect at structural weaknesses of the spring, helping to assess the impact of actual operating stress on fatigue life.

[0024] Furthermore, the working stress and stress concentration factor are analyzed together to calculate the actual working stress. The specific steps include multiplying the instantaneous stress in the stress time series by the corresponding stress concentration factor to obtain the actual working stress. Then, using the actual working stress data, combined with the material's SN curve, a new fatigue life assessment is performed. This combined analysis can more accurately reflect the stress state and fatigue life of the spring under actual working conditions, providing a more reliable basis for remaining life assessment.

[0025] Finally, based on the results of the joint analysis, the first estimated remaining life of the spring under current operating conditions is assessed. The specific steps include: calculating cumulative fatigue damage using actual operating stress data based on Miner's rule; and assessing the first estimated remaining life of the spring under current operating conditions based on the cumulative damage value. By considering load fluctuations and stress concentration effects, the first estimated remaining life assessment more closely reflects actual operating conditions, providing a scientific basis for spring maintenance and replacement.

[0026] Step S400: Based on the initial estimated remaining lifespan, introduce the environmental changes corresponding to the current working conditions, and perform a joint analysis of working stress and surface treatment method to obtain the second estimated remaining lifespan.

[0027] Specifically, data on environmental changes in the spring under current operating conditions is collected. This data is typically acquired in real time by multi-source monitoring devices such as temperature and humidity sensors. Environmental changes (such as temperature and humidity) directly affect the material properties and fatigue life of the spring, and are important factors in remaining life assessment.

[0028] Adjusting the working stress analysis based on environmental change data. Specific steps include: Temperature effect: High or low temperatures alter the stress-strain relationship and fatigue life of materials. Based on temperature sensor data, adjust the stress calculation formula to consider the impact of temperature on material strength and fatigue characteristics. Humidity effect: High humidity environments lead to material corrosion or performance degradation. Based on humidity sensor data, assess the impact of humidity on material properties and adjust the working stress calculation accordingly. Environmental changes cause fluctuations in material properties; these factors must be considered in the working stress analysis to obtain a more accurate stress assessment.

[0029] This study considers the impact of surface treatment methods (such as shot peening, heat treatment, electroplating, etc.) on material properties and fatigue life. Specific steps include: Surface treatment effect: Different surface treatment methods can improve the fatigue resistance and corrosion resistance of materials. Adjusting the stress concentration factor and fatigue life assessment parameters according to the spring's surface treatment method. Joint analysis: Combining the effects of environmental changes with the surface treatment method, a joint analysis is conducted to assess actual working stress and material properties. Surface treatment methods can significantly improve the fatigue performance of materials, and joint analysis can more accurately reflect the remaining life of the spring under actual working conditions.

[0030] Furthermore, the working stress and surface treatment method are analyzed together to calculate the actual working stress. Specific steps include: considering environmental changes and surface treatment methods, adjusting the stress concentration factor, calculating the actual working stress, and using the actual working stress data in conjunction with the material's SN curve to conduct a new fatigue life assessment. This combined analysis can more accurately reflect the stress state and fatigue life of the spring under actual working conditions, providing a more reliable basis for remaining life assessment.

[0031] Finally, based on the results of the joint analysis, the second estimated remaining life of the spring under current operating conditions is evaluated. The specific steps include: calculating cumulative fatigue damage using actual operating stress data based on Miner's rule; and evaluating the second estimated remaining life of the spring under current operating conditions based on the cumulative damage value. By considering environmental changes and surface treatment effects, the second estimated remaining life assessment more closely reflects actual working conditions, providing a scientific basis for spring maintenance and replacement.

[0032] Step S500: Based on the initial estimated remaining lifetime, the material weakness distribution corresponding to the current working conditions is introduced, and a joint analysis of stress concentration factor and surface treatment method is performed to obtain the third estimated remaining lifetime.

[0033] Specifically, data on the distribution of material weaknesses in the spring under current operating conditions is collected. This data can be acquired using strain sensors, vibration sensors, and image monitoring equipment. The distribution of material weaknesses (such as microcracks and surface defects) directly affects the fatigue performance and lifespan of the spring and is an important factor in assessing its remaining lifespan.

[0034] Considering the influence of material weakness distribution, the stress concentration factor is calculated. Specific steps include: Weakness identification: using sensors and image monitoring equipment to identify material weaknesses (such as cracks, notches, etc.). Stress concentration factor calculation: based on the identified material weaknesses, the stress concentration factor is calculated using finite element analysis or empirical formulas. The stress concentration factor reflects the stress amplification effect at material weaknesses and helps assess the impact of actual working stress on fatigue life.

[0035] This study considers the repair and enhancement effects of surface treatments (such as shot peening, heat treatment, and electroplating) on ​​material weaknesses. Specific steps include: Surface treatment effect: Different surface treatments can repair or alleviate material weaknesses and improve fatigue resistance. Joint analysis: Combining the effects of surface treatments with the distribution of material weaknesses, adjusting stress concentration factors and fatigue life assessment parameters. Surface treatments can significantly improve the fatigue resistance of materials, and joint analysis can more accurately reflect the remaining life of springs under actual working conditions.

[0036] Furthermore, the stress concentration factor and surface treatment method are analyzed together to calculate the actual working stress. Specific steps include: considering material weaknesses and surface treatment methods, adjusting the stress concentration factor, and calculating the actual working stress. Using the actual working stress data, combined with the material's SN curve, a new fatigue life assessment is performed. This combined analysis can more accurately reflect the stress state and fatigue life of the spring under actual working conditions, providing a more reliable basis for remaining life assessment.

[0037] Finally, based on the results of the joint analysis, the third estimated remaining life of the spring under current operating conditions is assessed. The specific steps include: calculating cumulative fatigue damage using actual operating stress data based on Miner's rule; and assessing the third estimated remaining life of the spring under current operating conditions based on the cumulative damage value. By considering the distribution of material weaknesses and surface treatment effects, the third estimated remaining life assessment more closely reflects actual operating conditions, providing a scientific basis for spring maintenance and replacement.

[0038] Step S600: Connect the nano-repair robot and use programmable materials to repair the target mechanical spring. After the repair is completed, introduce a multi-source monitoring device to obtain the load fluctuation sequence, environmental change sequence, and material weakness distribution map under the current working conditions. Integrate the first estimated remaining life, the second estimated remaining life, and the third estimated remaining life to obtain the remaining life assessment result under the current working conditions.

[0039] Specifically, a nano-repair robot is attached to a target mechanical spring using precision control equipment. The nano-repair robot can accurately locate and repair minute defects on the mechanical spring, restoring its original performance.

[0040] Nanoparticle repair robots are used to repair mechanical springs using programmable materials. Programmable materials can be deformed or altered under control to repair defects such as cracks and wear. The selection of programmable materials is crucial, considering both the spring material and the working environment. The nanoparticle repair robot uses programmable materials to fill cracks, wear, or other defects, restoring the structural integrity of the spring. The properties of programmable materials can be modified as needed to achieve efficient defect repair and extend the lifespan of the spring.

[0041] After repair, multi-source monitoring equipment is installed to monitor the mechanical spring in real time. This equipment includes pressure sensors, temperature sensors, humidity sensors, acceleration sensors, strain sensors, vibration sensors, and image monitoring devices. The multi-source monitoring equipment can acquire real-time data on the load, environmental conditions, and material state of the spring under actual operating conditions, providing detailed information on operating conditions. Using the multi-source monitoring equipment, load fluctuation sequences are obtained: load changes during the working cycle are recorded using pressure and acceleration sensors. Environmental change sequences are recorded using temperature and humidity sensors. Material weakness distribution maps are obtained: the location and distribution of material weaknesses (such as cracks and defects) are identified and recorded using strain sensors, vibration sensors, and image monitoring devices. The acquired multi-source data can be used to assess the actual operating condition of the spring after repair, providing fundamental data for remaining life assessment.

[0042] By integrating the first, second, and third estimated remaining lifespans with data from current operating conditions, a remaining life assessment is performed. Combining data on load fluctuations, environmental changes, and material weakness distribution with the estimated lifespan data yields a more accurate remaining lifespan assessment, guiding spring maintenance and replacement.

[0043] This step, through detailed repair and monitoring procedures and comprehensive analysis of multi-source data, yields a more accurate remaining life assessment. It considers various influencing factors under actual operating conditions, providing a scientific basis for the maintenance and replacement of mechanical springs, ensuring the reliability and safety of the system.

[0044] Furthermore, multi-source monitoring equipment is introduced to obtain load fluctuation sequences, environmental change sequences, and material weakness distribution maps under current operating conditions. This also includes: Based on the aforementioned multi-source monitoring equipment, an integrated sensor network is used to monitor the target mechanical spring under the current working conditions in real time and acquire multi-source monitoring data. The multi-source monitoring equipment includes pressure sensors, temperature sensors, humidity sensors, acceleration sensors, strain sensors, vibration sensors, and image monitoring equipment. Under the constraints of timing nodes, the load fluctuation data provided by the pressure sensor and acceleration sensor are analyzed to obtain the load fluctuation sequence under the current working conditions; Under the constraints of time sequence nodes, the environmental change data provided by the temperature sensor and humidity sensor are analyzed to obtain the environmental change sequence under the current working conditions; Based on strain sensors, vibration sensors, and image monitoring equipment, a distribution map of material weaknesses under current working conditions is constructed.

[0045] Specifically, multi-source monitoring equipment is configured and installed to monitor the condition of mechanical springs in real time under current operating conditions. This equipment includes pressure sensors, temperature sensors, humidity sensors, acceleration sensors, strain sensors, vibration sensors, and image monitoring devices. Multi-source monitoring equipment can collect physical and environmental data of mechanical springs under various operating conditions in real time, providing rich data support for remaining life assessment.

[0046] Integrating various sensors into a single sensor network ensures data synchronization and real-time transmission. This integration enables real-time acquisition and transmission of multi-source data, guaranteeing data integrity and consistency. By integrating the sensor network, the load, environmental conditions, and material status of mechanical springs can be monitored in real time, acquiring multi-source monitoring data. Real-time monitoring captures various changes in the mechanical springs during operation, providing fundamental data for accurately assessing their remaining lifespan.

[0047] Under time-series constraints, load fluctuation data provided by pressure and acceleration sensors are used for analysis. Pressure and acceleration data of the mechanical spring are recorded during its working cycle. Based on time-series analysis, a load fluctuation sequence is extracted to reflect the load variation over time. The load fluctuation sequence reflects the dynamic load experienced by the mechanical spring during actual operation and is an important input for fatigue life analysis.

[0048] Under time-series constraints, environmental change data provided by temperature and humidity sensors are analyzed. Temperature and humidity data of the mechanical spring are recorded during its working cycle. Based on time-series analysis, environmental change sequences are extracted to reflect the changes in temperature and humidity over time. These environmental change sequences can reflect the working state of the mechanical spring under different environmental conditions, providing a basis for evaluating material properties and fatigue life.

[0049] Strain sensors, vibration sensors, and image monitoring equipment are used to identify and record the distribution of material weaknesses (such as cracks and defects). Strain sensors record the strain of mechanical springs under different load conditions. Vibration sensors record the vibration of mechanical springs during operation. Image monitoring equipment captures minute cracks and defects on the surface of mechanical springs. The strain, vibration, and image data are fused and analyzed to construct a material weakness distribution map. This map accurately locates potential defects and weaknesses on mechanical springs, providing an important reference for remaining service life assessment.

[0050] For example, load data recorded by pressure and acceleration sensors yielded the following load fluctuation sequence: maximum load within the working cycle: 600N; minimum load within the working cycle: 100N; load change frequency: 10 times per second. Environmental data recorded by temperature and humidity sensors yielded the following environmental change sequence: temperature range: -20°C to 80°C; humidity range: 40% to 90%. Using strain sensors, vibration sensors, and image monitoring equipment, the locations of microcracks and surface defects on the mechanical spring were identified, and a material weakness distribution map was constructed. By integrating the load fluctuation sequence, environmental change sequence, and material weakness distribution map with the previously obtained first, second, and third predicted life data, the remaining life of the mechanical spring under the current working conditions was calculated.

[0051] Furthermore, under the constraint of timing nodes, the analysis of load fluctuation data provided by the pressure sensor and acceleration sensor also includes: Based on the pressure sensor and acceleration sensor, multiple target mechanical springs are traversed in the suspension unit where the target mechanical spring is located to obtain multiple load fluctuation sequences. In the suspension unit, load stability analysis is performed to obtain the load stability index; Based on the load stability index and combined with the load stability threshold, the maintenance cycle of multiple target mechanical springs in the suspension unit is set.

[0052] Specifically, pressure and acceleration sensors are used to iterate through multiple target mechanical springs in the suspension unit, recording their load fluctuation data at different time points. Within specific time points, each mechanical spring is monitored in real time, recording changes in pressure and acceleration. The monitoring data for each mechanical spring is used to generate a load fluctuation sequence, reflecting its load changes during the working cycle. By acquiring load fluctuation sequences from multiple mechanical springs, a comprehensive understanding of the load distribution and changes in the suspension system can be obtained.

[0053] The acquired load fluctuation sequences are analyzed to evaluate the load stability of each mechanical spring in the suspension unit. Statistical characteristics such as the load fluctuation range, mean, and standard deviation of each mechanical spring are calculated. Based on these statistical characteristics, the Load Stability Index (LSI) is calculated to measure the stability of load fluctuations. The LSI quantifies the load stability of mechanical springs under actual operating conditions, providing a reference for determining maintenance cycles.

[0054] Based on the load stability index and the load stability threshold, maintenance cycles for multiple target mechanical springs in the suspension unit are set. The load stability threshold is determined as the standard for judging maintenance needs. For mechanical springs with a load stability index below the threshold, a shorter maintenance cycle is set; for mechanical springs with a load stability index above the threshold, a longer maintenance cycle is set. The maintenance cycle of the mechanical springs is dynamically adjusted according to the actual load stability, thereby improving the maintenance efficiency and reliability of the suspension system.

[0055] For example, a car suspension system contains multiple mechanical springs. The above method is used to assess remaining life and set maintenance intervals. Pressure and acceleration sensors are used to monitor each mechanical spring at different time points, recording load fluctuation data. For example, the load fluctuation sequence for mechanical spring A is: 500N, 550N, 600N, 650N, 700N; the load fluctuation sequence for mechanical spring B is: 400N, 450N, 500N, 550N, 600N… Statistical analysis is performed on the load fluctuation sequences of each mechanical spring to calculate the load stability index. For example, the load stability index for mechanical spring A is 0.8, and for mechanical spring B it is 0.9… For mechanical springs with a load stability index below 0.85 (such as mechanical spring A), a shorter maintenance interval is set (e.g., every 3 months); for mechanical springs with a load stability index above 0.85 (such as mechanical spring B), a longer maintenance interval is set (e.g., every 6 months)…

[0056] This step, through real-time monitoring and data analysis, accurately assesses the load fluctuations and stability of each mechanical spring, dynamically adjusts maintenance cycles, and improves the maintenance efficiency and reliability of the suspension system. This method not only extends the service life of the mechanical springs but also reduces unnecessary maintenance costs.

[0057] Furthermore, under the constraint of time-series nodes, the analysis of environmental change data provided by the temperature and humidity sensors also includes: Based on the temperature sensor, a first material degradation channel is configured in a closed space using thermodynamic information and the material in the basic parameter information; Based on the temperature sensor, a temperature threshold is added, and temperature monitoring data that meets the temperature threshold is added to the multi-source monitoring data; Temperature monitoring data that does not meet the temperature threshold is used as high-temperature monitoring data. In the first material degradation channel, the influence of the high-temperature monitoring data on the spring material performance is analyzed to obtain the first associated influence feature and evaluate the heat resistance to obtain the high-temperature environmental adaptability of the target mechanical spring. The first associated influence feature includes yield strength and elastic modulus.

[0058] Specifically, based on temperature sensors, in a confined space, thermodynamic information and fundamental parameters of the mechanical spring (such as material type) are used to configure channels for evaluating the material's degradation under different temperature conditions. Thermodynamic information includes the material's coefficient of thermal expansion, thermal conductivity, and thermal stability. Fundamental parameters include the material type (e.g., steel, stainless steel), initial yield strength, and elastic modulus. By configuring material degradation channels, the performance changes of the mechanical spring under different temperature conditions are simulated, providing a data foundation for assessing its adaptability to high-temperature environments.

[0059] Based on a temperature sensor, a temperature threshold is set, and temperature monitoring data that meets this threshold is added to the multi-source monitoring data. The temperature threshold refers to setting a temperature range, such as -20°C to 80°C, as the normal operating temperature range. Temperature data within this range is recorded as monitoring data under normal temperature conditions. The normal operating temperature range is determined, and temperature data within this range is selected to provide basic data for subsequent analysis.

[0060] Temperature monitoring data that does not meet the temperature threshold are used as high-temperature monitoring data and analyzed in the first material degradation channel to assess the impact of high temperature on spring material properties. Temperature data exceeding the temperature threshold (e.g., above 80°C) are recorded. Using the high-temperature monitoring data, the effect of temperature changes on the material's yield strength and elastic modulus is analyzed in the degradation channel. The impact of high temperature on the mechanical spring material properties is assessed to determine its degradation under high-temperature conditions. Through the analysis of high-temperature monitoring data, the first correlation characteristics of high temperature on material properties are obtained, including: Yield strength: the change in the material's yield strength under high-temperature conditions; Elastic modulus: the change in the material's elastic modulus under high-temperature conditions. These characteristics directly reflect the changes in the material's mechanical properties under high-temperature conditions, providing a basis for heat resistance assessment.

[0061] Based on the first correlation influence characteristics, the adaptability of mechanical springs to high-temperature environments is evaluated. The service life and reliability of the material under high-temperature conditions are assessed based on changes in yield strength and elastic modulus. The adaptability index of the mechanical springs under high-temperature environments is calculated to determine their performance under specific high-temperature conditions. Through heat resistance evaluation, the working capacity and service life of the mechanical springs under high-temperature environments are determined, providing a scientific basis for developing maintenance and replacement plans.

[0062] This step, through detailed analysis of temperature change data and material degradation assessment methods, can accurately evaluate the remaining life and adaptability of mechanical springs in high-temperature environments, helping to develop reasonable maintenance and replacement plans and ensuring the reliability and safety of the system under high-temperature conditions.

[0063] Furthermore, the method includes: Based on the humidity sensor, a second material degradation channel is configured in an enclosed space using corrosion kinetics information and the materials in the basic parameter information; Based on the humidity sensor, a humidity threshold is added, and humidity monitoring data that meets the humidity threshold is added to the multi-source monitoring data; Humidity monitoring data that does not meet the humidity threshold is taken as high humidity monitoring data. In the second material degradation channel, the impact of the high humidity monitoring data on the spring material performance is analyzed, the second correlation influence feature is obtained, and the humidity resistance is evaluated to obtain the high humidity environment adaptability of the target mechanical spring. The second correlation influence feature includes the material corrosion rate.

[0064] Specifically, based on humidity sensors, in a confined space, corrosion kinetics information and fundamental parameters of mechanical springs (such as material type) are used to configure channels for assessing material degradation under different humidity conditions. Corrosion kinetics information includes the material's corrosion rate, oxidation reaction, and the effect of humidity on the material's surface chemical reactions. Fundamental parameters include the material type (e.g., steel, stainless steel), initial yield strength, and elastic modulus. By configuring material degradation channels, the performance changes of mechanical springs under different humidity conditions are simulated, providing a data foundation for assessing adaptability to high-humidity environments.

[0065] Based on a humidity sensor, a humidity threshold is set, and humidity monitoring data that meets this threshold is added to the multi-source monitoring data. The humidity threshold refers to setting a humidity range, such as 40% to 80%, as the normal operating humidity range. Humidity data within this range is recorded as monitoring data under normal humidity conditions. The normal operating humidity range is determined, and humidity data within this range is selected to provide basic data for subsequent analysis. Humidity monitoring data that does not meet the humidity threshold is treated as high humidity monitoring data and analyzed in the second material degradation channel to assess the impact of high humidity on spring material performance. Humidity data exceeding the humidity threshold (e.g., above 80%) is recorded. Using the high humidity monitoring data, the impact of humidity changes on material corrosion rate and performance is analyzed in the degradation channel. The impact of high humidity on the mechanical spring material performance is assessed, and its degradation status under high humidity conditions is determined. Through the analysis of high humidity monitoring data, the second correlation characteristics of high humidity on material performance are obtained, including: material corrosion rate: the change in the corrosion rate of the material under high humidity conditions. These characteristics directly reflect the changes in the chemical properties of the material under high humidity conditions, providing a basis for moisture resistance assessment.

[0066] Based on the second correlation influence characteristic, the adaptability of mechanical springs in high humidity environments is evaluated. The service life and reliability of the material under high humidity conditions are assessed according to the changes in material corrosion rate. The adaptability index of the mechanical springs in high humidity environments is calculated to determine their performance under specific high humidity conditions. Through humidity resistance assessment, the working capacity and service life of the mechanical springs in high humidity environments are determined, providing a scientific basis for developing maintenance and replacement plans.

[0067] This step, through detailed analysis of humidity variation data and material degradation assessment methods, can accurately evaluate the remaining lifespan and adaptability of mechanical springs in high humidity environments, helping to develop reasonable maintenance and replacement plans and ensuring the reliability and safety of the system under high humidity conditions.

[0068] Furthermore, the method includes: Connect the first material degradation channel and the second material degradation channel to obtain the material degradation model; Introducing the first associated influence feature and high temperature environment adaptability, the second associated influence feature and high humidity environment adaptability, and based on the material degradation model, adding frequent switching, which is regarded as an additional environmental factor; The material degradation model is connected to the nano-repair robot via communication. Based on the frequently switched work logs, repair instructions are issued, which include the optimal combination of repair parameters.

[0069] Specifically, a comprehensive material degradation model is established by connecting the first material degradation channel (high temperature effect) and the second material degradation channel (high humidity effect). The high temperature channel includes the degradation characteristics of materials under high temperature conditions, such as changes in yield strength and elastic modulus. The high humidity channel includes the degradation characteristics of materials under high humidity conditions, such as changes in material corrosion rate. By comprehensively considering the combined effects of high temperature and high humidity on materials, a complete material degradation model is constructed to more comprehensively evaluate material performance and lifespan.

[0070] The specific steps for constructing a material degradation model include: collecting basic data, including: material property data (yield strength, elastic modulus, hardness, tensile strength, ductility, etc.); environmental data (temperature, humidity, corrosive media, etc.); and working condition data (load, stress, frequent switching operations, etc.). Further, a high-temperature degradation model is established based on the changes in the mechanical properties of the material under high-temperature conditions. Data sources include high-temperature experimental data and literature data. Parameters include the relationship between yield strength and elastic modulus and temperature. A high-humidity degradation model is established based on the corrosion rate of the material under high-humidity conditions. Data sources include high-humidity experimental data and corrosion test data. Parameters include the relationship between the corrosion rate and mass loss rate of the material and humidity. A load influence model is established based on the stress distribution and fatigue characteristics of mechanical springs under different load conditions. Data sources include stress analysis and fatigue test data. Parameters include maximum stress, minimum stress, stress amplitude, and number of cycles. A frequent switching influence model is established based on the impact of frequent start-stop operations on material fatigue and wear. Data sources include operation logs and experimental data. The parameters include the number of start-stop cycles and operating frequency. Using multivariate regression analysis and finite element analysis, a comprehensive degradation model is established, integrating factors such as high temperature, high humidity, load, and frequent switching. This comprehensive degradation model can be expressed as D=f(T,H,σ,N,S), where T represents temperature, H represents humidity, σ represents stress, N represents the number of cycles, and S represents the number of start-stop cycles. The accuracy and reliability of the model are verified using experimental and actual operating data. Based on the verification results, the model parameters are adjusted to ensure that the model accurately reflects the actual situation. As more data is collected and analyzed, the model is dynamically updated to improve its predictive accuracy.

[0071] Furthermore, the first associated influence characteristic (changes in yield strength and elastic modulus under high temperature conditions) and high temperature environmental adaptability, and the second associated influence characteristic (material corrosion rate under high humidity conditions) and high humidity environmental adaptability are introduced into the material degradation model. This ensures that the material degradation model can reflect the actual changes in material properties and adaptability under high temperature and high humidity conditions.

[0072] Frequent switching (the operation of mechanical springs under high-frequency working conditions) is added as an additional environmental factor to the material degradation model. The operating log and frequency of the mechanical springs under frequent start-stop conditions are recorded. Frequent switching increases mechanical fatigue and wear, having an additional impact on material properties, and needs to be considered in the degradation model.

[0073] By establishing a communication link between a material degradation model and a nano-repair robot, repair commands can be issued in real time based on the model's evaluation results. This automates and automates the material degradation assessment and repair process, improving repair efficiency and accuracy.

[0074] Based on the evaluation results of the material degradation model and combined with the frequent switching operation logs, repair instructions are issued. These instructions include the optimal combination of repair parameters, such as repair time, repair location, and repair method. Based on the material degradation model and actual operating conditions, the best repair strategy is determined to maximize the recovery of material properties and extend service life. The repair process is ensured to be highly targeted and effective, extending the service life of mechanical springs and reducing downtime and maintenance costs.

[0075] This step, through detailed material degradation modeling and repair instruction issuance methods, accurately assesses the remaining life and repair needs of mechanical springs under complex environmental conditions, ensuring system reliability and safety. Simultaneously, it automates and intelligently integrates the assessment and repair process, improving maintenance efficiency and reducing downtime and maintenance costs.

[0076] Furthermore, based on the frequent switching operation logs, the repair commands issued also include: Based on the work log of the frequent switching, the cumulative impact of frequent switching on the performance of the spring material is analyzed to obtain the third related impact feature; Based on the first, second, and third correlation impact features, a multi-criteria decision analysis is performed to generate multiple repair parameter combinations corresponding to minor damage to the spring material. The repair parameter combinations include repair time and repair location. Based on the multiple repair parameter combinations, the repair time and repair location are encoded, and the fitness value of the minor damage to the spring material is evaluated using an objective function to obtain the optimal repair parameter combination.

[0077] Specifically, based on the frequent switching operation logs, the cumulative impact of frequent switching operations on the performance of mechanical spring materials is analyzed. The frequent switching operation logs are obtained, including the time, frequency, and duration of each start-stop cycle. Using material fatigue theory and fatigue cumulative damage models (such as Miner's rule), the cumulative fatigue damage to the material caused by frequent start-stop cycles is calculated. Frequent switching operations lead to fatigue accumulation in the material, affecting the performance and lifespan of the mechanical spring.

[0078] Based on the cumulative impact analysis of frequent switching, a third correlation impact characteristic was extracted. This third correlation impact characteristic includes cumulative fatigue damage value, material hardness change, and crack propagation rate. This third correlation impact characteristic reflects the long-term effects of frequent switching operations on material properties, providing crucial data for comprehensive life assessment.

[0079] Based on the first associated influence characteristic (changes in yield strength and elastic modulus under high temperature conditions), the second associated influence characteristic (material corrosion rate under high humidity conditions), and the third associated influence characteristic (cumulative fatigue damage), a multi-criteria decision analysis is conducted. All associated influence characteristics are integrated into a comprehensive decision model. Using multi-criteria decision methods (such as AHP and TOPSIS), the influence weights of each criterion on material properties are analyzed, generating multiple combinations of repair parameters. These repair parameter combinations include repair time, repair location, and repair method. Multi-criteria decision analysis can comprehensively consider various influencing factors, generate the optimal repair scheme, and improve repair efficiency and effectiveness.

[0080] Based on the generated combinations of repair parameters, the repair time and repair location are encoded. Each repair time and location is encoded with a unique identifier for easy identification and application in subsequent processes. Encoding facilitates systematic management of repair parameters and improves the execution efficiency of repair commands.

[0081] An objective function is used to evaluate the fitness of various repair parameter combinations for minor damage to the mechanical spring material. An objective function is defined, considering factors such as repair cost, repair effect, and material recovery degree, to evaluate the fitness value of each repair parameter combination. The fitness value of each repair parameter combination under the objective function is calculated, and the combination with the highest fitness is selected as the optimal repair scheme. Through objective function evaluation, the optimal repair scheme is selected to ensure the best repair effect and the lowest cost.

[0082] The objective function is constructed as follows: First, the main criteria affecting the repair of mechanical springs are defined: Repair cost (C): including costs related to materials, labor, and time. Repair effect (E): the degree of recovery of spring performance after repair, such as strength recovery rate and life extension. Material recovery degree (R): the recovery of material properties (such as yield strength, elastic modulus, corrosion resistance, etc.) after repair. Then, the above criteria are combined into a comprehensive objective function, as follows: + + ; in: , , These represent the weights of repair cost, repair effectiveness, and material recovery degree, reflecting the relative importance of each criterion in the decision-making process. C represents repair cost, with lower values ​​being better. E represents repair effectiveness, with higher values ​​being better, ranging from 0 to 1. R represents material recovery degree, with higher values ​​being better, ranging from 0 to 1.

[0083] This step, through detailed multi-criteria decision analysis and repair instruction issuance methods, accurately assesses the cumulative damage and repair needs of mechanical springs under complex environmental conditions, ensuring system reliability and safety. Simultaneously, it systematizes and optimizes the repair process, improving maintenance efficiency and reducing downtime and maintenance costs.

[0084] In summary, the method for assessing the remaining life of a mechanical spring provided in this application has the following technical advantages: 1. Fatigue analysis is performed by acquiring basic parameter information and historical usage data of mechanical springs to initially assess their remaining lifespan. Further analysis is then conducted considering various factors under current operating conditions (such as load fluctuations, environmental changes, and material weaknesses). Connecting a nano-repair robot enables real-time repair and monitoring, ensuring the accuracy and timeliness of the assessment and improving the reliability and lifespan of the springs.

[0085] 2. By analyzing the operation logs of frequent switching, the cumulative impact on material properties is determined, and the third correlation impact characteristics are obtained. Repair parameter combinations are generated through multi-criteria decision analysis, repair time and location are encoded, and the fitness value is evaluated using an objective function to obtain the optimal repair parameter combination. This improves repair effectiveness and cost-effectiveness, ensuring the reliability and service life of the mechanical spring.

[0086] Example 2 Based on the same inventive concept as the method for assessing the remaining life of a mechanical spring in the foregoing embodiments, such as Figure 2 As shown in the figure, this application provides a device for assessing the remaining life of a mechanical spring, the device comprising: Basic parameter information acquisition module 11: used to acquire basic parameter information of the target mechanical spring, wherein the basic parameter information includes material, diameter, free length, wire diameter, number of turns, pitch, spring unfolded length, and helix direction, and the diameter includes the inner diameter of the spring and the outer diameter of the spring; Fatigue analysis module 12: Used to perform fatigue analysis based on the basic parameter information and the historical usage data of the target mechanical spring, and to evaluate the initial expected remaining life of the spring under the current working conditions. The historical usage data includes the number of cycles, working length, and working load. Load fluctuation joint analysis module 13: Based on the initial estimated remaining life, it introduces the load fluctuation corresponding to the current working conditions, performs joint analysis of working stress and stress concentration factor, and obtains the first estimated remaining life; Environmental Change Joint Analysis Module 14: Based on the initial estimated remaining lifespan, it incorporates environmental changes corresponding to the current working conditions, performs joint analysis of working stress and surface treatment methods, and obtains a second estimated remaining lifespan. Weakness Distribution Joint Analysis Module 15: Based on the initial estimated remaining life, it introduces the material weakness distribution corresponding to the current working conditions, performs joint analysis of stress concentration factor and surface treatment method, and obtains the third estimated remaining life. Repair module 16: Used to connect to the nano-repair robot, use programmable materials to repair the target mechanical spring, and after the repair is completed, introduce multi-source monitoring equipment to obtain the load fluctuation sequence, environmental change sequence and material weakness distribution map under the current working conditions, and integrate the first estimated remaining life, the second estimated remaining life and the third estimated remaining life to obtain the remaining life assessment result under the current working conditions.

[0087] Furthermore, the device is also used to perform the following steps: Based on the aforementioned multi-source monitoring equipment, an integrated sensor network is used to monitor the target mechanical spring under the current working conditions in real time and acquire multi-source monitoring data. The multi-source monitoring equipment includes pressure sensors, temperature sensors, humidity sensors, acceleration sensors, strain sensors, vibration sensors, and image monitoring equipment. Under the constraints of timing nodes, the load fluctuation data provided by the pressure sensor and acceleration sensor are analyzed to obtain the load fluctuation sequence under the current working conditions; Under the constraints of time sequence nodes, the environmental change data provided by the temperature sensor and humidity sensor are analyzed to obtain the environmental change sequence under the current working conditions; Based on strain sensors, vibration sensors, and image monitoring equipment, a distribution map of material weaknesses under current working conditions is constructed.

[0088] Furthermore, the device is also used to perform the following steps: Based on the pressure sensor and acceleration sensor, multiple target mechanical springs are traversed in the suspension unit where the target mechanical spring is located to obtain multiple load fluctuation sequences. In the suspension unit, load stability analysis is performed to obtain the load stability index; Based on the load stability index and combined with the load stability threshold, the maintenance cycle of multiple target mechanical springs in the suspension unit is set.

[0089] Furthermore, the device is also used to perform the following steps: Based on the temperature sensor, a first material degradation channel is configured in a closed space using thermodynamic information and the material in the basic parameter information; Based on the temperature sensor, a temperature threshold is added, and temperature monitoring data that meets the temperature threshold is added to the multi-source monitoring data; Temperature monitoring data that does not meet the temperature threshold is used as high-temperature monitoring data. In the first material degradation channel, the influence of the high-temperature monitoring data on the spring material performance is analyzed to obtain the first associated influence feature and evaluate the heat resistance to obtain the high-temperature environmental adaptability of the target mechanical spring. The first associated influence feature includes yield strength and elastic modulus.

[0090] Furthermore, the device is also used to perform the following steps: Based on the humidity sensor, a second material degradation channel is configured in an enclosed space using corrosion kinetics information and the materials in the basic parameter information; Based on the humidity sensor, a humidity threshold is added, and humidity monitoring data that meets the humidity threshold is added to the multi-source monitoring data; Humidity monitoring data that does not meet the humidity threshold is taken as high humidity monitoring data. In the second material degradation channel, the impact of the high humidity monitoring data on the spring material performance is analyzed, the second correlation influence feature is obtained, and the humidity resistance is evaluated to obtain the high humidity environment adaptability of the target mechanical spring. The second correlation influence feature includes the material corrosion rate.

[0091] Furthermore, the device is also used to perform the following steps: Connect the first material degradation channel and the second material degradation channel to obtain the material degradation model; Introducing the first associated influence feature and high temperature environment adaptability, the second associated influence feature and high humidity environment adaptability, and based on the material degradation model, adding frequent switching, which is regarded as an additional environmental factor; The material degradation model is connected to the nano-repair robot via communication. Based on the frequently switched work logs, repair instructions are issued, which include the optimal combination of repair parameters.

[0092] Furthermore, the device is also used to perform the following steps: Based on the work log of the frequent switching, the cumulative impact of frequent switching on the performance of the spring material is analyzed to obtain the third related impact feature; Based on the first, second, and third correlation impact features, a multi-criteria decision analysis is performed to generate multiple repair parameter combinations corresponding to minor damage to the spring material. The repair parameter combinations include repair time and repair location. Based on the multiple repair parameter combinations, the repair time and repair location are encoded, and the fitness value of the minor damage to the spring material is evaluated using an objective function to obtain the optimal repair parameter combination.

[0093] In summary, any step of the method described above can be stored as a computer instruction or program in an unrestricted computer memory, and can be called and identified by an unrestricted computer processor to implement any method in the embodiments of this application, without any additional restrictions.

[0094] Furthermore, the "first" or "second" mentioned above may not only represent a sequential relationship, but may also represent a specific concept, and / or refer to the individual or collective selection of multiple elements. Clearly, those skilled in the art can make various modifications and variations to this application without departing from its scope. Therefore, if such modifications and variations fall within the scope of this application and its equivalents, this application intends to include such modifications and variations.

Claims

1. A method for assessing the remaining life of a mechanical spring, characterized in that, The method includes: Obtain the basic parameter information of the target mechanical spring, wherein the basic parameter information includes material, diameter, free length, wire diameter, number of turns, pitch, spring unfolded length, and helix direction, and the diameter includes the inner diameter of the spring and the outer diameter of the spring; Based on the aforementioned basic parameter information and the historical usage data of the target mechanical spring, fatigue analysis is performed to assess the initial expected remaining life of the spring under current working conditions. The historical usage data includes the number of cycles, working length, and working load. Based on the initial estimated remaining life, load fluctuations corresponding to the current working conditions are introduced, and joint analysis of working stress and stress concentration factor is performed to obtain the first estimated remaining life. Based on the initial estimated remaining lifespan, environmental changes corresponding to the current working conditions are introduced, and a joint analysis of working stress and surface treatment method is performed to obtain the second estimated remaining lifespan. Based on the initial estimated remaining lifetime, the material weakness distribution corresponding to the current working conditions is introduced, and a joint analysis of stress concentration factor and surface treatment method is performed to obtain the third estimated remaining lifetime. The system connects to a nano-repair robot and uses programmable materials to repair the target mechanical spring. After the repair is completed, a multi-source monitoring device is introduced to obtain the load fluctuation sequence, environmental change sequence, and material weakness distribution map under the current working conditions. The system integrates the first estimated remaining life, the second estimated remaining life, and the third estimated remaining life to obtain the remaining life assessment result under the current working conditions.

2. The method for assessing the remaining life of a mechanical spring as described in claim 1, characterized in that, The method involves introducing multi-source monitoring equipment to acquire load fluctuation sequences, environmental change sequences, and material weakness distribution maps under current operating conditions. Based on the aforementioned multi-source monitoring equipment, an integrated sensor network is used to monitor the target mechanical spring under the current working conditions in real time and acquire multi-source monitoring data. The multi-source monitoring equipment includes pressure sensors, temperature sensors, humidity sensors, acceleration sensors, strain sensors, vibration sensors, and image monitoring equipment. Under the constraints of timing nodes, the load fluctuation data provided by the pressure sensor and acceleration sensor are analyzed to obtain the load fluctuation sequence under the current working conditions; Under the constraints of time sequence nodes, the environmental change data provided by the temperature sensor and humidity sensor are analyzed to obtain the environmental change sequence under the current working conditions; Based on strain sensors, vibration sensors, and image monitoring equipment, a distribution map of material weaknesses under current working conditions is constructed.

3. The method for assessing the remaining life of a mechanical spring as described in claim 2, characterized in that, Under the constraints of timing nodes, the load fluctuation data provided by the pressure sensor and acceleration sensor are analyzed, and the method includes: Based on the pressure sensor and acceleration sensor, multiple target mechanical springs are traversed in the suspension unit where the target mechanical spring is located to obtain multiple load fluctuation sequences. In the suspension unit, load stability analysis is performed to obtain the load stability index; Based on the load stability index and combined with the load stability threshold, the maintenance cycle of multiple target mechanical springs in the suspension unit is set.

4. The method for assessing the remaining life of a mechanical spring as described in claim 2, characterized in that, Under the constraints of time-series nodes, the method for analyzing environmental change data provided by the temperature sensor and humidity sensor includes: Based on the temperature sensor, a first material degradation channel is configured in a closed space using thermodynamic information and the material in the basic parameter information; Based on the temperature sensor, a temperature threshold is added, and temperature monitoring data that meets the temperature threshold is added to the multi-source monitoring data; Temperature monitoring data that does not meet the temperature threshold is used as high-temperature monitoring data. In the first material degradation channel, the influence of the high-temperature monitoring data on the spring material performance is analyzed to obtain the first associated influence feature and evaluate the heat resistance to obtain the high-temperature environmental adaptability of the target mechanical spring. The first associated influence feature includes yield strength and elastic modulus.

5. The method for assessing the remaining life of a mechanical spring as described in claim 4, characterized in that, The method includes: Based on the humidity sensor, a second material degradation channel is configured in an enclosed space using corrosion kinetics information and the materials in the basic parameter information; Based on the humidity sensor, a humidity threshold is added, and humidity monitoring data that meets the humidity threshold is added to the multi-source monitoring data; Humidity monitoring data that does not meet the humidity threshold is taken as high humidity monitoring data. In the second material degradation channel, the impact of the high humidity monitoring data on the spring material performance is analyzed, the second correlation influence feature is obtained, and the humidity resistance is evaluated to obtain the high humidity environment adaptability of the target mechanical spring. The second correlation influence feature includes the material corrosion rate.

6. The method for assessing the remaining life of a mechanical spring as described in claim 5, characterized in that, The method includes: Connect the first material degradation channel and the second material degradation channel to obtain the material degradation model; Introducing the first associated influence feature and high temperature environment adaptability, the second associated influence feature and high humidity environment adaptability, and based on the material degradation model, adding frequent switching, which is regarded as an additional environmental factor; The material degradation model is connected to the nano-repair robot via communication. Based on the frequently switched work logs, repair instructions are issued, which include the optimal combination of repair parameters.

7. The method for assessing the remaining life of a mechanical spring as described in claim 6, characterized in that, Based on the frequent switching operation logs, a repair command is issued. The method includes: Based on the work log of the frequent switching, the cumulative impact of frequent switching on the performance of the spring material is analyzed to obtain the third related impact feature; Based on the first, second, and third correlation impact features, a multi-criteria decision analysis is performed to generate multiple repair parameter combinations corresponding to minor damage to the spring material. The repair parameter combinations include repair time and repair location. Based on the multiple repair parameter combinations, the repair time and repair location are encoded, and the fitness value of the minor damage to the spring material is evaluated using an objective function to obtain the optimal repair parameter combination.

8. A device for assessing the remaining life of a mechanical spring, characterized in that, The apparatus for performing the method according to any one of claims 1 to 7 comprises: Basic parameter information acquisition module: used to acquire the basic parameter information of the target mechanical spring, wherein the basic parameter information includes material, diameter, free length, wire diameter, number of turns, pitch, spring unfolded length, and helix direction, and the diameter includes the inner diameter of the spring and the outer diameter of the spring; Fatigue analysis module: used to perform fatigue analysis based on the basic parameter information and the historical usage data of the target mechanical spring, and to evaluate the initial expected remaining life of the spring under the current working conditions. The historical usage data includes the number of cycles, working length, and working load. Load fluctuation joint analysis module: Based on the initial estimated remaining life, it introduces the load fluctuation corresponding to the current working conditions, performs joint analysis of working stress and stress concentration factor, and obtains the first estimated remaining life; Environmental Change Joint Analysis Module: Based on the initial estimated remaining lifespan, this module incorporates environmental changes corresponding to the current working conditions to perform a joint analysis of working stress and surface treatment methods, thereby obtaining a second estimated remaining lifespan. Weakness Distribution Joint Analysis Module: Based on the initial estimated remaining life, this module introduces the material weakness distribution corresponding to the current operating conditions, performs joint analysis of stress concentration factor and surface treatment method, and obtains the third estimated remaining life. Repair module: Used to connect to the nano-repair robot, use programmable materials to repair the target mechanical spring. After the repair is completed, multi-source monitoring equipment is introduced to obtain the load fluctuation sequence, environmental change sequence and material weakness distribution map under the current working conditions. The first estimated remaining life, the second estimated remaining life and the third estimated remaining life are integrated to obtain the remaining life assessment result under the current working conditions.

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