An application of an electronic depth protection agent for improving the wear resistance of connectors

Abstract

The present invention discloses an application for improving the wear resistance of connectors based on an electronic depth protection agent, which relates to the field of power technology. In the present invention, the data collected in real time by the sensor is combined with the simulation results of the digital twin model to dynamically adapt to complex environments, predict and control the progress of wear, so that the connectors can maintain good performance under different working conditions; the wear trend is predicted through a deep learning model, potential high-wear areas are identified in advance, and the thickness of the protective layer and the lubrication frequency are adjusted by the electronic depth protection agent, so that the thickness of the protective layer changes synchronously with the wear state, significantly delaying the wear speed, reducing the number and time of maintenance. By predicting the wear trend, the service life of key components can be estimated, and maintenance personnel can reasonably arrange the maintenance cycle according to the actual state of the equipment, avoiding premature or late maintenance, optimizing the maintenance time, and also avoiding the cost waste caused by over-maintenance, thereby improving the efficiency of equipment management.

Description

Technical Field

[0001] The present invention relates to the field of power technology, and in particular to an application of an electronic deep protection agent for improving the wear resistance of connectors. Background Art

[0002] Connectors are key basic components in mechanical and electronic systems, mainly used for transmitting power and signals, and are widely used in fields such as industrial automation, aerospace, energy, medical devices, and transportation; in these environments, connectors often face high-frequency vibrations, complex loads, and extreme temperature and humidity conditions, so extremely high requirements are placed on their wear resistance and reliability. Traditional methods for improving the wear resistance of connectors mainly rely on material selection or surface coatings under specific environments. However, under complex working conditions, the wear rate is difficult to predict, the maintenance cost is relatively high, and the service life is relatively short.

[0003] To solve the above problems, traditional solutions usually improve in terms of materials and structures. For example, high-hardness or self-lubricating materials are used to improve the anti-wear performance, or corrosion-resistant coatings are used to delay the influence of environmental factors on materials; in addition, some connector designs also use composite structures or distributed loads to reduce the contact stress and lower the wear risk; these methods enhance the wear resistance of connectors at the physical level, enabling them to effectively resist mechanical friction and environmental erosion within a certain period of time. However, although these solutions perform well under experimental conditions, in actual working conditions, the wear speed and type are often difficult to predict, and traditional material and structure improvements are difficult to provide dynamic feedback and adjustment to the wear process, resulting in a lack of adaptability in wear-resistant designs.

[0004] In addition, such traditional solutions lack real-time wear monitoring and life prediction means, making it difficult to detect potential wear problems in a timely manner. Once wear occurs, it is often irreversible, which may lead to increased overall equipment loss and even cause failures, not only increasing the maintenance cost but also being unable to optimize the maintenance cycle and increasing the risk of sudden system failures. Therefore, there is an urgent need for an application solution for improving the wear resistance of connectors based on electronic deep protection agents to solve such problems. Summary of the Invention

[0005] In view of the above existing problems, the present invention is proposed.

[0006] The present invention provides an application of an electronic deep protection agent for improving the wear resistance of connectors to solve the problems that the current wear resistance methods lack real-time data feedback during use, are difficult to accurately judge the wear state and remaining life of connectors, lack intelligent monitoring and control means, and are difficult to meet the durability requirements of connectors under complex working conditions.

[0007] To solve the above technical problems, the present invention provides the following technical solutions:

[0008] The present invention provides an application for improving the wear resistance of a connector based on an electronic depth protection agent, which includes,

[0009] Step S1, establishing a digital twin model,

[0010] Based on the geometric structure and material properties of the connector, a digital twin model is constructed to simulate the force, wear, and temperature changes of the connector under different environments;

[0011] Step S2, wear prediction and life estimation based on deep learning,

[0012] Based on the historical and real-time data obtained in step S1 and the simulation results, a deep learning model is constructed and trained to identify the correlation between the wear rate and environmental parameters, and a wear trend graph is formed;

[0013] Step S3, adaptive optimization and dynamic application of the electronic depth protection agent,

[0014] Combined with the wear prediction data in step S2, an optimization algorithm is used to adjust the pressure and load distribution at the contact part of the connector to reduce stress in the high-wear area;

[0015] Introduce the electronic depth protection agent in the high-wear area to form an intelligent lubricating protective layer: increase the lubrication frequency in the high-wear or high-temperature area according to the wear severity and real-time temperature;

[0016] The electronic depth protection agent forms a thin dynamic protective layer on the contact surface, isolates direct friction, reduces the wear rate, and improves the wear-resistant life.

[0017] Furthermore, in step S1, real-time data is obtained: at the key parts of the connector, including the contact surface, contact pins, and locking parts, micro sensors are installed to collect temperature, stress, and vibration data in real time. At the same time, temperature, humidity, and dust environment sensors are arranged to obtain the environmental condition data of the connector;

[0018] The real-time data includes: temperature, stress, and vibration data as well as environmental condition data; the real-time data is input into the digital twin model to dynamically simulate the wear and stress distribution of the connector in the actual use environment, and the simulation results are output.

[0019] Furthermore, in step S1, the steps of constructing the digital twin model include:

[0020] Based on the geometric structure and material properties of the connector, the digital twin model is initialized, and the force and stress distribution modeling of the connector is carried out; the Archard wear model is used to predict wear according to the contact pressure, sliding distance, and material wear coefficient: Where W represents the wear amount, k is the wear coefficient, F is the normal force on the contact surface, d is the sliding distance, and H is the hardness of the material;

[0021] Build a thermo-mechanical coupling model, and model the temperature field for the frictional heating under high load. Let the temperature field be T(x, y, z, t), then: where ρ is the material density, c is the specific heat capacity of the material, T(x, y, z, t) is the temperature field, which varies with position (x, y, z) and time t, and k th is the thermal conductivity, q is the frictional heat generation term, q = τ·v, where v is the sliding velocity and τ is the shear stress;

[0022] Obtain real-time data from sensors and input the real-time data into the digital twin model for dynamic simulation update.

[0023] Furthermore, in step S2, the deep learning model predicts the wear development trend and the life limit of key parts based on the input data, providing early warning for the upcoming wear risk; the input data is historical and real-time data as well as simulation results;

[0024] At the same time, the deep learning model identifies potential high-wear areas.

[0025] Furthermore, in step S2, the method for wear prediction and life estimation based on deep learning is as follows:

[0026] Let the historical wear data be W hist , the real-time temperature be T real , the real-time stress be σ real , the real-time vibration frequency be f vib , the real-time contact pressure be p real , and the simulation output data of the digital twin model be W sim . Based on the above data, preprocessing and feature extraction are performed. The features include: wear rate R wear and the environmental feature combination, where ΔW is the wear amount within a certain time interval, Δt is the time interval, and the environmental feature combination is defined as, which is used as the input of the deep learning model;

[0027] Adopt a neural network model based on long short-term memory (LSTM) to capture the time series features of wear. Let the input be the time series feature X env and the historical wear rate R wear (t), and the output be the predicted future wear rate The loss function is defined as the mean square error between the predicted wear rate and the actual rate: where L represents the prediction error, N represents the total number of time steps, t i represents the time step, i represents the time step index, and Δt is the adjacent time step interval.

[0028] Further, in step S2, the methods for wear prediction and life estimation also include:

[0029] Based on the predicted future wear rate Calculate the cumulative wear of the key parts of the connector:

[0030] where Δt is the adjacent time step interval, and W cumulative (t) and W cumulative (t + Δt) are the cumulative wear amounts of two adjacent time steps respectively;

[0031] Let the endurance limit be W limit , and estimate the remaining life of the key parts using the current wear amount and the endurance limit:

[0032] where t life represents the remaining time until the wear endurance reaches the limit, that is, the remaining life;

[0033] According to the real-time data and simulation results, apply the local wear distribution function W local (x, y) to identify the high wear area: where R wear (x, y, τ) is the wear rate at the position (x, y) at time τ, and analyze the high-value area of W local (x, y) to identify potential high wear positions.

[0034] Further, in step S3, the digital twin model and the deep learning model are linked, and according to the wear state feedback by the sensor, the electronic depth protection agent and lubrication parameters are adjusted in real time to synchronize the protective layer with the wear change.

[0035] Further, in step S3, combined with the wear prediction data in step S2, adjust the pressure distribution of the contact part of the connector. Specifically:

[0036] Define the objective function J of the total stress in the high wear area. The function describes the deviation between the current stress distribution and the target stress distribution. The optimization purpose is to adjust the pressure distribution p(x, y) to make the actual stress close to the target value. The objective function J is defined as: J = ∫ Ω (σ(x, y) - σ goal ) 2 dA, where Ω is the high wear area, dA represents any infinitesimal area element in the integration area Ω, σ(x, y) represents the actual stress at the position (x, y), calculated based on the current pressure p(x, y), and σ goal is the desired target stress value, used to optimize the stress concentration area, reduce wear, and minimize J to reduce the stress concentration in the wear area;

[0037] The actual stress σ(x, y) = α·p(x, y), where α is the proportionality coefficient and p(x, y) is the contact pressure at the position (x, y). Adjust p(x, y) to control the distribution of σ(x, y) to make it close to the target stress σ goal ;

[0038] Add the constraint conditions:

[0039] Total load constraint: The total load P within the contact area total remains constant, that is, ∫ Ω p(x, y)dA = P total ;

[0040] Non - negative pressure constraint: The contact pressure p(x, y) is non - negative, that is, p(x, y) ≥ 0;

[0041] Adopt the Lagrange multiplier method to combine the objective function with the constraint conditions, and use an optimization algorithm to iteratively adjust the contact pressure p(x, y). Define the Lagrangian function:

[0042] where λ is the Lagrange multiplier, solve and to obtain the optimized distribution of p(x, y);

[0043] Based on the pressure distribution of the optimization result, update the contact pressure in the high - wear area to relieve the stress concentration in these areas in real time.

[0044] Furthermore, in step S3, the way to adjust the protective agent and lubrication parameters is as follows:

[0045] Based on the wear amount W(t) and wear rate R(t) fed back by the sensor, calculate the optimal thickness h(t) of the electronic depth protective agent in real time. h(t) = β·R(t), where h(t) is the thickness of the protective agent at time t, is the wear rate, and β is the adjustment coefficient;

[0046] Based on the predicted future wear rate from the deep - learning model dynamically adjust the lubrication frequency:

[0047] where f(t) is the lubrication frequency, f min is the minimum lubrication frequency, γ is the adjustment parameter, is the predicted wear rate at time t + Δt. When the predicted wear rate increases, the lubrication frequency automatically increases, so as to strengthen the protection in the area where wear intensifies.

[0048] Furthermore, in step S3, the way to adjust the protective agent and lubrication parameters also includes:

[0049] Feedback the wear state to the digital twin model, calculate the thickness distribution h(x, y, t) of the protective agent and the lubrication frequency f(x, y, t) at each position, and use the optimization objective function J to dynamically balance the usage amount and coverage effect of the protective agent:

[0050] Among them, J is the objective function, representing the deviation between the wear rate and the coverage effect of the protective agent; Ω is the contact area, α is the proportional coefficient, related to the effect of the protective agent, h(x, y, t) is the thickness of the protective agent at the position (x, y) at time t, and f(x, y, t) is the lubrication frequency at this position;

[0051] The linkage control strategy is as follows: input sensor data to collect the real-time wear amount and wear rate; model calculation: calculate the thickness of the protective agent and the lubrication frequency according to the current wear state; optimization adjustment: feedback h(x, y, t) and f(x, y, t), and the protective layer dynamically adapts to the current wear situation.

[0052] The beneficial effects of the present invention are as follows:

[0053] In the present invention, through the digital twin model and the deep learning algorithm, the real-time monitoring and simulation of the wear rate and the environment are realized. By combining the data collected by the sensor in real time with the simulation results of the digital twin model, it can dynamically adapt to the complex environment, predict and control the progress of wear, so that the connector can maintain good performance under different working conditions; predict the wear trend through the deep learning model, identify potential high-wear areas in advance, and adjust the thickness of the protective layer and the lubrication frequency through the electronic deep protective agent, so that the thickness of the protective layer changes synchronously with the wear state, significantly delaying the wear speed, reducing the maintenance times and time, and lowering the maintenance cost.

[0054] In the present invention, according to the wear prediction data, the lubrication frequency and the thickness of the protective agent can be adjusted in real time and dynamically, making the use of the protective agent more intelligent and precise, reducing the waste of materials, and also prolonging the duration of the protective effect.

[0055] The present invention can predict the wear trend, thereby estimating the service life of key components. Maintenance personnel can reasonably arrange the maintenance cycle according to the actual state of the equipment, avoid premature or late maintenance, optimize the maintenance time, and also avoid the cost waste caused by over-maintenance, improving the efficiency of equipment management. Description of the Drawings

[0056] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0057] Figure 1 Schematic diagram of the application method for improving the wear resistance of a connector based on an electronic depth protection agent according to the present invention. Specific embodiments

[0058] To make the above objects, features, and advantages of the present invention more obvious and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings of the specification.

[0059] In the following description, many specific details are set forth to facilitate a thorough understanding of the present invention. However, the present invention may be practiced in other ways different from those described herein. Those skilled in the art can make similar generalizations without departing from the spirit of the present invention. Therefore, the present invention is not limited by the specific embodiments disclosed below.

[0060] Secondly, the so-called "one embodiment" or "embodiment" herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The appearances of "in one embodiment" in different places in this specification do not all refer to the same embodiment, nor are they separate or alternative embodiments that exclude each other.

[0061] Example 1, referring to Figure 1 , this example provides an application for improving the wear resistance of a connector based on an electronic depth protection agent, including the following steps:

[0062] Step S1, establish a digital twin model,

[0063] Based on the geometric structure and material properties of the connector, construct a digital twin model to simulate the force, wear, and temperature changes of the connector under different environments;

[0064] Obtain real-time data in step S1: Install micro sensors at key parts of the connector, including the contact surface, contact pins, and locking parts, to collect temperature, stress, and vibration data in real time. At the same time, arrange temperature, humidity, and dust environment sensors to obtain the environmental condition data of the connector;

[0065] The real-time data includes: temperature, stress, and vibration data, as well as environmental condition data; Input the real-time data into the digital twin model to perform dynamic simulation on the wear and stress distribution of the connector in the actual use environment, and output the simulation results;

[0066] In step S1, the steps of constructing the digital twin model include:

[0067] Initialize the digital twin model based on the geometric structure and material properties of the connector, and perform modeling of the force and stress distribution of the connector; Use the Archard wear model to predict wear based on the contact pressure, sliding distance, and material wear coefficient: Among them, W represents the wear amount, k is the wear coefficient, F is the normal force on the contact surface, d is the sliding distance, and H is the hardness of the material;

[0068] Construct a thermo-mechanical coupling model, and model the temperature field for the friction temperature rise under high load. Let the temperature field be T(x, y, z, t), then: Among them, ρ is the material density, c is the specific heat capacity of the material, T(x, y, z, t) is the temperature field, which changes with the position (x, y, z) and time t, and k th is the thermal conductivity, q is the friction heat generation term, q = τ·v, where v is the sliding speed and τ is the shear stress;

[0069] Obtain real-time data from the sensor and input the real-time data into the digital twin model for dynamic simulation update;

[0070] Specifically, based on the geometric and material characteristics of the connector, establish a digital twin model to simulate the force, temperature, and wear distribution in the real environment, provide real-time simulation data, combine with the data feedback from the actual sensor, and dynamically reflect the wear state of the connector under different working conditions; predict the wear trend through the deep learning model, identify the relationship between the wear rate and environmental parameters, analyze the wear trend to estimate the life of key parts, provide timely warnings for high-wear areas, and take preventive measures in advance before the high-wear areas occur.

[0071] Step S2, Wear prediction and life estimation based on deep learning,

[0072] Based on the historical and real-time data obtained in step S1 and the simulation results, construct and train a deep learning model to identify the relationship between the wear rate and environmental parameters and form a wear trend chart;

[0073] In step S2, the deep learning model predicts the wear development trend and the life limit of key parts according to the input data, and provides early warnings for upcoming wear risks; the input data is historical and real-time data and simulation results;

[0074] At the same time, the deep learning model identifies potential high-wear areas;

[0075] In step S2, the method of wear prediction and life estimation based on deep learning is as follows:

[0076] Let the historical wear data be W hist , the real-time temperature be T real , the real-time stress be σ real , the real-time vibration frequency be f vib , the real-time contact pressure be p real , and the simulation output data of the digital twin model be W sim, preprocess and extract features based on the above data. The features include: wear rate R wear and the combination of environmental features, where ΔW is the wear amount within a certain time interval, Δt is the time interval, and the combination of environmental features is defined as the input to the deep learning model;

[0077] Use a neural network model based on Long Short-Term Memory (LSTM) to capture the time series features of wear. Let the input be the time series feature X env and the historical wear rate R wear (t), and the output be the predicted future wear rate The loss function is defined as the mean square error between the predicted wear rate and the actual rate: where L represents the prediction error, N represents the total number of time steps, t i represents the time step, i represents the time step index, and Δt is the adjacent time step interval;

[0078] In step S2, the methods for wear prediction and life estimation also include:

[0079] Based on the predicted future wear rate calculate the cumulative wear amount of the key parts of the connector:

[0080] where Δt is the adjacent time step interval, W cumulative (t) and W cumulative (t + Δt) are the cumulative wear amounts of two adjacent time steps respectively;

[0081] Let the endurance limit be w limit , and estimate the remaining life of the key parts using the current wear amount and the endurance limit:

[0082] where t life represents the remaining time until the wear endurance reaches the limit, that is, the remaining life;

[0083] According to the real-time data and simulation results, apply the local wear distribution function w local (x, y) to identify the high wear areas: where R wear (x, y, τ) is the wear rate at the position (x, y) at time τ, and analyze the high value areas of W local (x, y) to identify potential high wear positions;

[0084] Specifically, with the support of the prediction data, use an optimization algorithm to intelligently adjust the contact pressure and load distribution to accurately reduce the stress concentration in the high wear areas.

[0085] Step S3, Adaptive optimization and dynamic application of electronic depth protectant,

[0086] Combined with the wear prediction data in step S2, an optimization algorithm is used to adjust the pressure and load distribution at the contact part of the connector, and reduce stress in the high-wear area;

[0087] Introduce an electronic depth protectant in the high-wear area to form an intelligent lubricating protective layer: according to the wear severity and real-time temperature, increase the lubrication frequency in the high-wear or high-temperature area;

[0088] The electronic depth protectant forms a thin dynamic protective layer on the contact surface, isolates direct friction, reduces the wear rate, and improves the wear-resistant life;

[0089] In step S3, the digital twin model and the deep learning model are linked, and according to the wear state feedback by the sensor, the electronic depth protectant and lubrication parameters are adjusted in real time to synchronize the protective layer with the wear change;

[0090] In step S3, combined with the wear prediction data in step S2, adjust the pressure distribution at the contact part of the connector. Specifically:

[0091] Define the objective function I of the total stress in the high-wear area. The function describes the deviation between the current stress distribution and the target stress distribution. The optimization purpose is to adjust the pressure distribution p(x, y) to make the actual stress close to the target value. The objective function J is defined as: J = ∫ Ω (σ(x, y) - σ goal ) 2 dA, where Ω is the high-wear area, dA represents any tiny area unit in the integral area Ω, σ(x, y) represents the actual stress at the position (x, y), calculated based on the current pressure p(x, y), σ goal is the desired target stress value, used to optimize the stress concentration area, reduce wear, and minimize J to reduce the stress concentration in the wear area;

[0092] The actual stress σ(x, y) = α·p(x, y), where α is the proportionality coefficient, p(x, y) is the contact pressure at the position (x, y), adjust p(x, y) to control the distribution of σ(x, y) to make it close to the target stress σ goal ;

[0093] Add constraint conditions:

[0094] Total load constraint: The total load P in the contact area total remains constant, that is, ∫ Ω p(x, y)dA = P total ;

[0095] Pressure non - negative constraint: The contact pressure p(x, y) is non - negative, i.e., p(x, y) ≥ 0;

[0096] Using the Lagrange multiplier method, the objective function and the constraint conditions are combined, and an optimization algorithm is used to iteratively adjust the contact pressure p(x, y). Define the Lagrangian function:

[0097] where λ is the Lagrange multiplier, and solve and to obtain the optimized distribution of p(x, y);

[0098] Based on the pressure distribution of the optimization result, update the contact pressure in the high - wear areas to relieve the stress concentration in these areas in real - time;

[0099] In step S3, the ways to adjust the protective agent and lubrication parameters are as follows:

[0100] Based on the wear amount W(t) and wear rate R(t) feedback by the sensor, calculate the optimal thickness h(t) of the electronic depth protective agent in real - time. h(t)=β·R(t), where h(t) is the thickness of the protective agent at time t, is the wear rate, and β is the adjustment coefficient;

[0101] Based on the predicted future wear rate dynamically adjust the lubrication frequency:

[0102] where f(t) is the lubrication frequency, f min is the minimum lubrication frequency, γ is the adjustment parameter, is the predicted wear rate at time t + Δt. When the predicted wear rate increases, the lubrication frequency automatically increases, so as to strengthen the protection in the areas where wear intensifies;

[0103] In step S3, the ways to adjust the protective agent and lubrication parameters also include:

[0104] Feed the wear state back to the digital twin model, calculate the protective agent thickness distribution h(x, y, t) and lubrication frequency f(x, y, t) at each position, and use the optimization objective function J to dynamically balance the usage amount and coverage effect of the protective agent:

[0105] where J is the objective function, representing the deviation between the wear rate and the coverage effect of the protective agent. Ω is the contact area, α is the proportionality coefficient related to the protective agent effect, h(x, y, t) is the thickness of the protective agent at time T at position (x, y), and f(x, y, t) is the lubrication frequency at this position;

[0106] The linkage control strategy is:

[0107] Sensor data input to collect real-time wear amount and wear rate;

[0108] Model calculation: Calculate the thickness of the protective agent and the lubrication frequency according to the current wear state;

[0109] Optimization and adjustment: Feed back h(x, y, t) and f(x, y, t), and the protective layer dynamically adapts to the current wear situation;

[0110] Specifically, dynamic linkage control of the protective agent and lubrication parameters is achieved in the high-wear area. The digital twin model and the deep learning model continuously optimize the distribution of the protective agent and the lubrication frequency according to the real-time wear data. Based on the Lagrangian optimization algorithm, the contact pressure distribution in the high-wear area is adjusted to minimize the deviation between the actual stress and the target stress, thereby reducing the degree of wear concentration and improving the reliability and stability of the connector in extreme environments.

[0111] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention, and they should all be covered within the scope of the claims of the present invention.

Claims

1. An application of an electronic depth protection agent for improving the wear resistance of connectors, characterized in that: The application steps include: Step S1: Establish a digital twin model. Based on the geometric structure and material properties of the connector, construct a digital twin model to simulate the force, wear, and temperature changes of the connector under different environments. Step S2: Wear prediction and life estimation based on deep learning. Based on the historical and real-time data obtained in Step S1 and the simulation results, construct and train a deep learning model to identify the correlation between the wear rate and environmental parameters, and form a wear trend graph. Step S3: Adaptive optimization and dynamic application of an electronic deep protection agent. Combining the wear prediction data in Step S2, use an optimization algorithm to adjust the pressure and load distribution at the contact part of the connector, and reduce stress in high-wear areas. Introduce an electronic deep protection agent in high-wear areas to form an intelligent lubricating protective layer: Increase the lubrication frequency in high-wear or high-temperature areas according to the wear severity and real-time temperature. In Step S3, combining the wear prediction data in Step S2, adjust the pressure distribution at the contact part of the connector. Specifically: Define the objective function J for the total stress within the high-wear region. The function describes the deviation between the current stress distribution and the target stress distribution. The optimization aim is to adjust the pressure distribution p(x, y) so that the actual stress approaches the target value. The objective function J is defined as: J = ∫ Ω (σ(x, y) - σ goal ) 2 dA, where Ω is the high-wear region, dA represents any infinitesimal area element within the integration region Ω, σ(x, y) represents the actual stress at the position (x, y), calculated based on the current pressure p(x, y), and σ goal is the desired target stress value, used to optimize the stress concentration region and reduce wear; The actual stress σ(x, y) = α·p(x, y), where α is the proportionality coefficient and p(x, y) is the contact pressure at the position (x, y). Adjust p(x, y) to control the distribution of σ(x, y) to make it close to the target stress σ goal ; Add constraint conditions: Total load constraint: the total load P within the contact area total remains constant, i.e., ∫ Ω p(x,y)dA = P total ; Pressure non-negativity constraint: The contact pressure p(x, y) is non-negative, that is, p(x, y) ≥ 0. Use the Lagrange multiplier method to combine the objective function with the constraint conditions, and use an optimization algorithm to iteratively adjust the contact pressure p(x, y). Define the Lagrangian function: where λ is the Lagrange multiplier, solving and to obtain the optimized p(x,y) distribution; In Step S3, the method of adjusting the protection agent and lubrication parameters is: Based on the wear amount W(t) and wear rate R(t) feedback from the sensor, the optimal thickness h(t) of the electronic depth protection agent is calculated in real time, where h(t) = β·R(t). Here, h(t) is the thickness of the protection agent at time t, R(t) is the wear rate, and β is the adjustment coefficient; Future Wear Rate Predicted Based on Deep Learning Model Dynamically Adjust Lubrication Frequency: where f(t) is the lubrication frequency, f min is the minimum lubrication frequency, γ is the adjustment parameter, is the predicted wear rate at time t + Δt. When the predicted wear rate increases, the lubrication frequency automatically increases; In Step S3, the method of adjusting the protection agent and lubrication parameters also includes: Feed back the wear state into the digital twin model, calculate the thickness distribution h(x, y, t) of the protection agent and the lubrication frequency f(x, y, t) at each position, and use the optimization objective function J to dynamically balance the usage amount and coverage effect of the protection agent. Among them, J is the objective function, representing the deviation between the wear rate and the coverage effect of the protective agent; Ω is the contact area; α is the proportionality coefficient, related to the effect of the protective agent; h(x, y, t) is the thickness of the protective agent at time t at position (x, y); f(x, y, t) is the lubrication frequency at this position.

2. The application of an electronic depth protector for improving the wear resistance of a connector according to claim 1, wherein Obtain real-time data in Step S1: Install micro sensors at key parts of the connector, including the contact surface, contact pins, and locking parts, to collect temperature, stress, and vibration data in real time. At the same time, arrange temperature, humidity, and dust environment sensors to obtain the environmental condition data of the connector. The real-time data includes: temperature, stress, and vibration data, as well as environmental condition data; Input the real-time data into the digital twin model to perform dynamic simulation on the wear and stress distribution of the connector in the actual use environment, and output the simulation results.

3. The application of an electronic depth protection agent for improving the wear resistance of a connector according to claim 2, characterized in that, In Step S1, the steps of constructing the digital twin model include: Initialize the digital twin model based on the geometric structure and material properties of the connector, and model the force and stress distribution of the connector; use the Archard wear model to predict wear based on the contact pressure, sliding distance, and material wear coefficient: Among them, W represents the wear amount, k is the wear coefficient, F is the normal force on the contact surface, d is the sliding distance, and H is the hardness of the material; Construct a thermo-mechanical coupling model, and model the temperature field for the frictional heating under high load. Let the temperature field be T(x, y, z, t), then: where ρ is the material density, c is the specific heat capacity of the material, T(x, y, z, t) is the temperature field, which varies with position (x, y, z) and time t, and k th is the thermal conductivity, q is the frictional heat generation term, q = τ·v, where v is the sliding velocity and τ is the shear stress; Obtain real-time data from the sensors and input the real-time data into the digital twin model for dynamic simulation update.

4. An application for improving the abrasion resistance of a connector based on an electronic depth protection agent according to claim 3, characterized in that, In Step S2, the deep learning model predicts the wear development trend and the life limit of key parts based on the input data, and provides early warnings for upcoming wear risks. The input data is historical and real-time data, as well as simulation results. At the same time, the deep learning model identifies potential high-wear areas.

5. The application of an electronic depth protection agent for improving the wear resistance of a connector according to claim 4, characterized in that, In Step S2, the method of wear prediction and life estimation based on deep learning is: Let the historical wear data be W hist , the real-time temperature be T real , the real-time stress be σ real , the real-time vibration frequency be f vib , the real-time contact pressure be p real , and the simulation output data of the digital twin model be W sim , preprocessing and feature extraction are performed based on the above data, and the features include: wear rate R wear and the environmental feature combination where ΔW is the wear amount within a certain time interval, Δt is the time interval, and the environmental feature combination is defined as, serving as the input of the deep learning model; Use a neural network model based on long short-term memory (LSTM) to capture the time series features of wear. Let the input be the time series feature X env and the historical wear rate R wear (t), and the output be the predicted future wear rate The loss function is defined as the mean squared error between the predicted wear rate and the actual rate: where L represents the prediction error, N represents the total number of time steps, t i represents the time step, i represents the time step index, and Δt is the adjacent time step interval.

6. The application of an electronic depth protection agent for improving the wear resistance of a connector according to claim 5, characterized in that, In Step S2, the method of wear prediction and life estimation also includes: Predicted future wear rate Calculate the cumulative wear of the critical parts of the connector: where Δt is the adjacent time step interval, and W cumulative (t) and W cumulative (t + Δt) are the cumulative wear amounts at two adjacent time steps respectively; Let the endurance limit be W limit , and estimate the remaining life of the key parts by using the current wear amount and the endurance limit: Among them, t life represents the remaining time until the wear durability reaches the limit, that is, the remaining life; Based on real-time data and simulation results, apply the local wear distribution function W local (x,y) to identify high-wear areas: where R wear (x,y,τ) is the wear rate at position (x,y) at time τ, and analyze the high-value areas of W local (x,y) to identify potential high-wear positions.

7. An application for improving the abrasion resistance of a connector based on an electronic depth protection agent according to claim 6, characterized in that, In Step S3, the digital twin model and the deep learning model are linked. According to the wear state feedback by the sensors, the electronic deep protection agent and lubrication parameters are adjusted in real time to synchronize the protective layer with the wear changes.

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