Mechanical performance enhancement system for prefabricated optical cable fiber connector of smart substation

Abstract

The application discloses a prefabricated optical cable fiber connector mechanical performance enhancement system of a smart substation, relates to the technical field of optical communication of the smart substation, and comprises a data acquisition module, an environmental stress modeling module, a risk simulation module and a strategy fusion module. The system collects connector parameters and in-station environmental data, constructs a comprehensive stress model, analyzes vibration interference spectrum and natural frequency by using a modal matching device, generates a resonance avoidance strategy, analyzes periodic thermal stress and connector structure by using a thermodynamic simulator, generates a deformation compensation scheme, and finally fuses and outputs a composite enhancement strategy. The system can accurately predict and avoid the resonance risk of the connector and a specific electromagnetic vibration source, effectively compensate the cumulative deformation caused by the periodic thermal stress, and thus improve the long-term mechanical reliability and optical transmission stability of the connector in a complex in-station environment before deployment.

Description

Technical Field

[0001] This invention belongs to the field of optical communication technology for smart substations, specifically a mechanical performance enhancement system for prefabricated optical fiber connectors in smart substations. Background Technology

[0002] In the construction and operation of smart substations, prefabricated optical cables are widely used to achieve reliable fiber optic connections between equipment. The fiber optic connectors of these prefabricated cables are exposed to the complex physical environment of the substation for extended periods, facing specific spectral vibrations generated by the operation of electromagnetic equipment and the significant temperature cycling effects caused by day-night cycles and seasonal changes. These two environmental stresses are the main factors leading to the degradation of connector mechanical performance, increased insertion loss, and even communication interruptions.

[0003] Existing protection technologies mostly employ general methods. For vibration, passive reinforcement measures such as adding clips or using damping materials are typically taken after installation, or a rough estimate is made during the design phase based on general vibration standards. For temperature effects, the focus is mainly on the high and low temperature tolerance ratings of the connector materials themselves, or static temperature cycling tests are conducted. These methods fail to deeply analyze the precise electromagnetic vibration spectrum characteristics unique to substation sites and excited by the operation of primary equipment, nor do they model temperature changes as a dynamic process that generates periodic mechanical stress.

[0004] Existing technologies struggle to accurately predict the risk of fiber optic connectors resonating with major electromagnetic vibration sources in specific substation environments before deployment. Furthermore, they cannot proactively compensate for the cumulative deformation and fatigue of the connector structure caused by cyclic thermal stress through structural design or installation processes. This leads to potential long-term mechanical reliability issues in prefabricated optical cables, often resulting in loosening due to resonance or hidden damage due to thermal fatigue during operation and maintenance, requiring frequent repairs. Summary of the Invention

[0005] This invention aims to solve at least one of the technical problems existing in the prior art; Therefore, this invention proposes a system for enhancing the mechanical performance of prefabricated optical fiber connectors in intelligent substations, comprising: The data acquisition module acquires the set of physical parameters of the prefabricated optical cable to be deployed in the smart substation and the set of expected service environment data. The set of physical parameters of the prefabricated optical cable includes the material property data, structural size data and natural frequency data of the optical fiber connector. The set of expected service environment data includes the electromagnetic vibration interference spectrum, temperature difference range data and mechanical stress application point information. The environmental stress modeling module extracts vibration spectrum features based on the electromagnetic vibration interference spectrum to obtain core vibration mode features, performs temperature stress analysis based on the temperature difference range data to obtain periodic thermal stress features, and constructs a comprehensive environmental stress model by combining the mechanical stress application point information. The risk simulation module inputs the natural frequency data of the fiber optic connector and the core vibration mode characteristics into the mode matcher to calculate the resonance risk and generate a resonance avoidance strategy. It also inputs the material property data and structural dimension data of the fiber optic connector and the periodic thermal stress characteristics into the thermodynamic simulator to analyze the connector deformation and generate a thermal stress deformation compensation scheme. The strategy fusion module integrates the resonance avoidance strategy and the thermal stress deformation compensation scheme to generate a composite performance enhancement strategy for the fiber optic connector.

[0006] Furthermore, vibration spectrum features are extracted based on the electromagnetic vibration interference spectrum to obtain core vibration mode features, including: Bandpass filtering is performed on the electromagnetic vibration interference spectrum to separate the potential resonant frequency band related to the physical size of the optical fiber connector; Peak detection and mode decomposition are performed within the potential resonance frequency band to identify the dominant vibrational frequency components and their corresponding amplitude and phase information; Based on the dominant vibration frequency components, amplitude, and phase information, the vibration waveform that has the main impact on the optical fiber connector in the spatial dimension is reconstructed. Principal component analysis is performed on the reconstructed vibration waveform to extract the core waveform parameters that determine its dynamic response, and these core waveform parameters are used as the core vibration mode features.

[0007] Furthermore, the inherent frequency data of the fiber optic connector and the core vibration mode characteristics are input into a mode matcher to calculate the resonance risk and generate a resonance avoidance strategy, including: A frequency-response relationship matrix is ​​established in the modal matcher, wherein the frequency-response relationship matrix uses the order frequencies in the natural frequency data as the reference rows and the dominant vibration frequency components in the core vibration mode features as the reference columns. Calculate the modal superposition coefficient corresponding to each element in the frequency-response relationship matrix. The modal superposition coefficient represents the potential energy of resonance between the natural frequency of a connector of a specific order and a specific dominant vibration frequency component. Elements whose modal superposition coefficients exceed a preset safety threshold are selected to identify high-risk resonance frequency pairs; For each high-risk resonant frequency pair, the required frequency offset or damping increment is calculated to form a frequency tuning suggestion or additional damping configuration suggestion. All suggestions are then combined to form the resonant avoidance strategy.

[0008] Furthermore, the material property data and structural dimension data of the fiber optic connector, along with the periodic thermal stress characteristics, are input into a thermodynamic simulator for connector deformation analysis to generate a thermal stress deformation compensation scheme, including: In the thermodynamic simulator, a three-dimensional geometric model of the fiber optic connector is established based on the structural dimension data; The coefficient of thermal expansion, elastic modulus, and yield strength parameters from the material property data are assigned to the three-dimensional geometric model; The periodic thermal stress characteristics are applied as boundary conditions to the surface of the three-dimensional geometric model and key connection parts, and transient thermal-structural coupling simulation calculations are performed. Obtain the stress distribution cloud map and cumulative plastic strain distribution of the optical fiber connector obtained from simulation calculations over a complete temperature cycle; By analyzing the stress distribution cloud map and cumulative plastic strain distribution, weak areas prone to yielding or fatigue failure are identified. For each of the weak regions, a pre-compensation geometry is designed or a gradient material transition zone is introduced. The pre-compensation geometry or gradient material transition zone is used to offset the deformation caused by thermal stress. All designs together constitute the thermal stress deformation compensation scheme.

[0009] Furthermore, by integrating the resonance avoidance strategy and the thermal stress deformation compensation scheme, a composite performance enhancement strategy for the fiber optic connector is generated, including: A strategy fusion space is established, and the frequency tuning suggestions or additional damping configuration suggestions in the resonance avoidance strategy, as well as the pre-compensation geometry or gradient material transition zone design in the thermal stress deformation compensation scheme, are all mapped to multi-dimensional vectors in the strategy fusion space. Conflict detection is performed in the strategy fusion space to identify mutually restrictive enhancements in terms of spatial location or material properties; Cooperative optimization calculations are performed on conflicting enhancement measures to generate compromise or phased implementation optimization schemes, so that enhancement measures from different sources can coexist physically and complement each other functionally. All optimized enhancement measures are grouped and sorted according to their implementation area on the fiber optic connector to form a structured implementation plan list, which is the composite performance enhancement strategy.

[0010] Furthermore, the step of performing collaborative optimization calculations on conflicting enhancement measures to generate compromise or phased implementation optimization schemes includes: Quantitatively assess the contribution weight of each enhancement measure involved in each conflict to the final mechanical performance enhancement target; Based on the contribution weights, a multi-objective optimization function is constructed, which aims to maximize the overall enhancement effect while minimizing the physical interference between measures. A heuristic search algorithm is used to solve the multi-objective optimization function, resulting in a set of non-dominated solutions. From the set of non-dominated solutions, a solution that balances implementation feasibility and enhancement effect is selected according to the preset engineering implementation priority, and is used as the optimized scheme for the compromise or phased implementation.

[0011] Furthermore, it also includes: The instruction generation module, based on the composite performance enhancement strategy and combined with the specific model information of the optical fiber connector, generates detailed design instructions including additional damping structure parameters and local material enhancement schemes. The additive manufacturing module inputs the detailed design instructions into the intelligent additive manufacturing device, which then drives the device to perform structural printing and material cladding on the fiber optic connector body, forming a finished fiber optic connector with enhanced mechanical properties.

[0012] Furthermore, based on the aforementioned composite performance enhancement strategy and combined with the specific model information of the fiber optic connector, detailed design instructions are generated, including additional damping structure parameters and local material reinforcement schemes, comprising: From the structured implementation plan list of the composite performance enhancement strategy, the types of enhancement measures that need to be implemented at specific locations on the fiber optic connector are analyzed; Based on the specific model information of the fiber optic connector, the corresponding 3D part model library is called to obtain the 3D model of the connector. On the three-dimensional model, specific three-dimensional coordinates of the implementation area are matched for each type of enhancement measure; For reinforcement measures of the additional damping structure type, calculate its mass, stiffness and installation interface dimensions in the three-dimensional coordinates of the implementation area, and generate the parameters of the additional damping structure; For reinforcement measures of the local material reinforcement type, the material composition gradient, cladding layer thickness and bonding layer parameters in the three-dimensional coordinates of the implementation area are calculated to generate the local material reinforcement scheme. The additional damping structure parameters are integrated with the local material reinforcement scheme and associated with the corresponding coordinates on the three-dimensional model to form the detailed design instructions; The reinforcement measures for localized material reinforcement types calculate the material composition gradient, cladding layer thickness, and bonding layer parameters in the three-dimensional coordinates of the implementation area, specifically including: Based on the yield strength and toughness requirements of the weak areas in the thermal stress deformation compensation scheme, determine the target material performance curve required for the target reinforcement area; Based on the target material performance curve, two or more base materials are selected from a pre-set material database, and their compositional variation path from the connector substrate to the surface of the reinforcing layer is planned in space to generate the material composition gradient. Based on the magnitude of thermal stress in the corresponding region obtained from thermodynamic simulation, the minimum material increment required to resist deformation is calculated, and the thickness of the cladding layer is determined by considering process allowance. Based on the compatibility data of the selected base material and the connector substrate material, the material composition and thickness of the intermediate transition layer are designed as parameters of the bonding layer.

[0013] Furthermore, the detailed design instructions are input into the intelligent additive manufacturing device, which then performs structural printing and material cladding on the fiber optic connector body to form a finished fiber optic connector with enhanced mechanical properties, including: The controller of the intelligent additive manufacturing device parses the detailed design instructions and separates the structure printing path instructions and the material cladding process parameter instructions. According to the structural printing path instructions, the printing nozzle is controlled to deposit special polymer or metal material for forming additional damping structure at a designated position on the fiber optic connector body, following a preset path. Under the control of the material cladding process parameters, the high-energy beam source is activated, and according to the set material composition gradient, cladding layer thickness and bonding layer parameters, the reinforcing material is clad layer by layer in the designated area of ​​the optical fiber connector body to achieve metallurgical bonding with the substrate. During the structural printing and material cladding process, an integrated on-machine measurement system monitors the forming dimensions and molten pool status in real time, compares them with the expected values ​​in the detailed design instructions, and generates real-time process adjustment signals to feed back to the controller to dynamically fine-tune the printing or cladding parameters.

[0014] Furthermore, during the structural printing and material cladding process, the integrated on-machine measurement system monitors the forming dimensions and molten pool state in real time, compares them with the expected values ​​in the detailed design instructions, and generates real-time process adjustment signals, including: The laser scanning probe acquires the three-dimensional point cloud data of the deposited structure or cladding layer in real time, and calculates the dimensional deviation between the data and the corresponding three-dimensional coordinates of the area in the detailed design instructions. The temperature field distribution and morphological characteristics of the molten pool during the cladding process are monitored in real time using an infrared thermal imager or a high-speed camera, and parameters such as the width, depth and tail cooling rate of the molten pool are extracted. The dimensional deviation, melt pool width, depth, and tail cooling rate parameters are input into a pre-trained process quality prediction model to predict the potential defect risk of the final formed part under the current parameters. If the predicted potential defect risk exceeds the allowable threshold, the process quality prediction model outputs adjustment suggestions, which include adjusting the printing speed, laser power, or wire feed speed. These adjustment suggestions serve as the real-time process adjustment signal.

[0015] Compared with the prior art, the beneficial effects of the present invention are: This method extracts core vibration mode characteristics based on electromagnetic vibration interference spectra and calculates resonance risk by comparing these characteristics with the connector's natural frequencies using a mode matcher. This approach identifies the most critical frequency components posing the greatest threat to connectors from complex field vibration data, rather than handling vibrations across the entire frequency band. Through the mode matcher's calculations, it's possible to definitively determine whether the connector structure will fall into the resonance range in the expected environment during the design and planning phase before fiber optic cable deployment. Based on this, avoidance strategies can be generated, such as adjusting the installation position, changing the clamp stiffness, or modifying local connector structures. This transforms protective measures from broad, empirical reinforcement to targeted, simulation-based proactive design, directly preventing premature mechanical failure and signal transmission instability caused by resonance effects.

[0016] The characteristics of periodic thermal stress are analyzed based on temperature difference range data, and deformation analysis is performed using a thermodynamic simulator combined with the specific materials and dimensions of the connector. This process transforms temperature field changes into periodic mechanical loads acting on the connector structure for simulation, rather than simply evaluating the temperature adaptability of the materials. The simulation results reveal where stress concentration occurs during repeated thermal expansion and contraction of the connector, what deformation modes will recur, and what may lead to a decrease in ferrule alignment. The compensation scheme generated based on this analysis can guide the design of connector structures to reserve deformation space, select combined materials with complementary coefficients of thermal expansion, or specify optimal preload in installation procedures to offset the cumulative misalignment caused by periodic thermal stress. This achieves proactive containment of thermally induced performance degradation and maintains the insertion loss stability of the optical link under long-term temperature cycling. Attached Figure Description

[0017] Figure 1 This is a timing diagram of the mechanical performance enhancement system for prefabricated optical fiber connectors in intelligent substations according to the present invention. Figure 2 A flowchart for extracting core vibration mode features; Figure 3 A flowchart generated for a thermal stress deformation compensation scheme; Figure 4 This is a line graph showing the gradient distribution of material composition. Figure 5 This diagram represents the resonance risk assessment and thermal stress deformation analysis. Detailed Implementation

[0018] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] See Figure 1 The data acquisition module acquires the physical parameter set of the prefabricated optical cable to be deployed in the smart substation and the expected service environment data set. The prefabricated optical cable physical parameter set includes the material property data, structural dimension data, and natural frequency data of the optical fiber connector. The expected service environment data set includes electromagnetic vibration interference spectrum, temperature difference range data, and mechanical stress application point information. The environmental stress modeling module extracts the core vibration mode characteristics based on the electromagnetic vibration interference spectrum, performs temperature stress analysis based on the temperature difference range data to obtain periodic thermal stress characteristics, and constructs a comprehensive environmental stress model by combining the mechanical stress application point information. The risk simulation module inputs the natural frequency data and core vibration mode characteristics of the optical fiber connector into the mode matcher to calculate resonance risk and generate resonance avoidance strategies. It inputs the material property data, structural dimension data, and periodic thermal stress characteristics of the optical fiber connector into the thermodynamic simulator to analyze connector deformation and generate thermal stress deformation compensation schemes. The strategy fusion module integrates the resonance avoidance strategy and the thermal stress deformation compensation scheme to generate a composite performance enhancement strategy for the optical fiber connector.

[0020] See Figure 2 In one embodiment of the present invention, the electromagnetic vibration interference spectrum included in the expected service environment data set of the smart substation is a frequency-amplitude data sequence from 0 Hz to 5000 Hz. The data acquisition module obtains this raw data from the substation monitoring system. The environmental stress modeling module performs bandpass filtering on the electromagnetic vibration interference spectrum. The passband range of the filter is set based on the typical structural resonant frequency range corresponding to the physical size of the fiber optic connector, for example, 100 Hz to 2000 Hz, separating the potential resonant frequency band. In a specific implementation, peak detection and mode decomposition are performed within the potential resonant frequency band to identify three dominant vibration frequency components. The values ​​of the dominant vibration frequency components are 125 Hz, 480 Hz, and 1550 Hz, respectively. At the same time, the amplitude and phase information corresponding to each dominant vibration frequency component are recorded. In some embodiments, the vibration waveform is reconstructed based on the dominant vibration frequency components, amplitude, and phase information. The reconstruction process involves superimposing the sine waves of each frequency component according to their amplitude and phase to generate a time-space coupled vibration waveform function. Principal component analysis is performed on the reconstructed vibration waveform to extract core waveform parameters. The core waveform parameters include the waveform contribution rate of the first principal component and the corresponding characteristic frequency vector. The core waveform parameters are output as core vibration mode features.

[0021] In some embodiments, the mode matcher receives natural frequency data of the fiber optic connector, including a first natural frequency of 215 Hz and a second natural frequency of 1580 Hz. A frequency-response matrix is ​​established in the mode matcher, with each order frequency in the natural frequency data as the base row and the dominant vibrational frequency component in the core vibrational mode characteristics as the base column. The modal superposition coefficient corresponding to each element in the frequency-response matrix is ​​calculated. The modal superposition coefficient characterizes the potential energy for resonance between a specific order connector natural frequency and a specific dominant vibrational frequency component. In one example, the modal superposition coefficient is calculated using the following formula:

[0022] in: Representing the The inherent frequency of the first-order connector and the second-order connector Modal superposition coefficients between the dominant vibrational frequency components It is the first The amplitude of the dominant vibrational frequency component, It is the first The inherent frequency of the step connector, It is the first The frequency values ​​of the dominant vibration frequency components are determined. Elements with modal superposition coefficients exceeding a preset safety threshold (set to 5.0) are identified, thus determining high-risk resonant frequency pairs. In practice, the required frequency offset or damping increment is calculated for each high-risk resonant frequency pair. For a high-risk pair consisting of a natural frequency of 1580 Hz and a dominant vibration frequency of 1550 Hz, the calculated frequency offset is 35 Hz, resulting in a frequency tuning recommendation to adjust the connector's natural frequency to 1615 Hz. All frequency tuning recommendations and additional damping configuration recommendations are combined to form a resonance avoidance strategy. It is understood that additional damping configuration recommendations include the type and mass parameters of damping material added at specific locations on the connector housing. Optionally, the calculation of the modal superposition coefficient can integrate more vibration mode parameters, but the basic calculation principle follows energy proximity assessment.

[0023] In practice, the preset safety threshold is determined based on the engineering safety factor and historical failure data. It is understood that the frequency offset calculation must ensure that the adjusted natural frequency is far removed from all identified dominant vibration frequency components and their harmonic frequency ranges. Optionally, the specific parameters for the additional damping configuration recommendations are generated by querying a preset damping material performance database. In practice, the resulting resonance avoidance strategy is a structured data list, where each item corresponds to a high-risk resonance frequency pair and its handling recommendations.

[0024] See Figure 3In one embodiment of the present invention, a thermodynamic simulator establishes a three-dimensional geometric model based on the structural dimensional data of the fiber optic connector. The structural dimensional data includes the length and diameter of the fiber optic connector shell and the precise dimensions of the internal ferrule, for example, the shell length is 25 mm and the diameter is 8 mm. In a specific implementation, the coefficient of thermal expansion, elastic modulus, and yield strength parameters from the material property data are assigned to the three-dimensional geometric model. The coefficient of thermal expansion is 5.5e-6 Kelvin, the elastic modulus is 210 GPa, and the yield strength is 300 MPa. In some embodiments, periodic thermal stress characteristics are applied as boundary conditions to the surface of the three-dimensional geometric model and key connection parts. The periodic thermal stress characteristics are derived from the temperature difference range data in the expected service environment dataset, for example, the temperature difference range is from -40 degrees Celsius to +85 degrees Celsius. Transient thermo-structural coupling simulation calculations are performed, with the simulation time step set to 1 second, and the total duration covering a complete temperature cycle.

[0025] In practical implementation, the stress distribution cloud map and cumulative plastic strain distribution of the fiber optic connector obtained from simulation calculations over a complete temperature cycle are acquired. The stress distribution cloud map shows that the maximum stress occurs at the junction of the fiber optic connector shell and the ferrule, with a value of 280 MPa. Analysis of the stress distribution cloud map and cumulative plastic strain distribution identifies weak areas prone to yielding or fatigue failure. The criteria for determining weak areas are that the stress value exceeds 80% of the material's yield strength or the cumulative plastic strain exceeds 0.2%. In one example, the formula for determining weak areas is:

[0026] in: Represents risk factors in vulnerable areas. It is the von Mises stress obtained from simulation. It is the yield strength of the material. It is cumulative plastic strain. This is the critical plastic strain threshold. When the risk factor of a weak region is greater than 1.0, the region is determined to be a weak region. It can be understood that the critical plastic strain threshold is set to 0.002 based on the material fatigue characteristics. Optionally, weak region identification can be based on gradient change regions in the stress contour map, such as regions with stress gradients exceeding 50 MPa per millimeter.

[0027] In specific implementations, a pre-compensation geometry is designed for each weak area, or a gradient material transition zone is introduced. For the stress-concentrated shell joint, the pre-compensation geometry is designed by adding a chamfer with a radius of 0.5 mm at the joint. In some embodiments, for areas with high cumulative plastic strain, a gradient material transition zone is introduced. The gradient material transition zone continuously changes the material composition from the fiber optic connector matrix material to the high-toughness alloy. This continuous change in material composition is described by a composition gradient function, for example, a linear transition from 100% matrix material to 100% high-toughness alloy with a transition distance of 2 mm. It can be understood that the design of the pre-compensation geometry is based on offsetting the deformation caused by thermal stress, achieved by modifying the local dimensions of the three-dimensional geometric model in the weak area, for example, adjusting the chamfer radius from 0.2 mm to 0.5 mm. Optionally, the design parameters of the gradient material transition zone include the composition change rate and layer thickness. The composition change rate is set to a 5% change in alloy content per micrometer, and the layer thickness is set to 200 micrometers. In practice, all pre-compensation geometric designs and gradient material transition zone designs constitute a thermal stress deformation compensation scheme. The thermal stress deformation compensation scheme is output in the form of three-dimensional model modification instructions and material distribution maps. The three-dimensional model modification instructions contain the specific coordinates and dimensions of geometric adjustments, and the material distribution maps contain the spatial composition distribution data of the gradient material transition zone.

[0028] In one embodiment of the invention, the established strategy fusion space is a multi-dimensional vector space, whose dimensions correspond to all physical and material properties on the fiber optic connector that can be reinforced. Frequency tuning proposals in resonance avoidance strategies are mapped as vectors, with vector components including the proposed natural frequency value, the adjusted frequency value, the implementation location coordinates, and the change in damping coefficient. Pre-compensation geometry designs in thermal stress deformation compensation schemes are mapped as vectors, with vector components including the geometry modification type, modified size parameters, and modified region coordinates. Gradient material transition zone designs are mapped as vectors, with vector components including the material composition gradient function, transition layer thickness, and region coordinates. In some embodiments, conflict detection is performed in the strategy fusion space. The conflict detection rule is to determine whether different vectors overlap in spatial coordinates and whether they mutually restrict each other in material properties. For example, a damping structure vector requiring an increase in local mass conflicts with a material reinforcement vector requiring a reduction in local mass to reduce thermal inertia in the same coordinate region, identifying three pairs of reinforcement measures that mutually restrict each other in spatial location or material properties.

[0029] In practical implementation, collaborative optimization calculations are performed on conflicting enhancement measures to quantify and evaluate the contribution weight of each enhancement measure involved in each conflict to the final mechanical performance enhancement target. The mechanical performance enhancement targets include the resonance risk reduction rate and the thermal stress fatigue life improvement rate. The contribution weights are determined using the analytic hierarchy process (AHP). Damping structure adjustment has a weight of 0.7 for the resonance risk reduction rate and 0.1 for the thermal stress fatigue life improvement rate; material gradient enhancement has a weight of 0.8 for the thermal stress fatigue life improvement rate and 0.2 for the resonance risk reduction rate. A multi-objective optimization function is constructed based on the contribution weights. The expression of the multi-objective optimization function is as follows:

[0030] in: Represents the overall enhancement effect. This represents the rate of reduction in resonance risk resulting from a single measure. This represents the rate of increase in fatigue life resulting from a single measure. and These are the normalized target weight coefficients. The sum of physical interference between representative measures It is a measure vector and The represented measures are a function of the degree of conflict in space and materials. A non-dominated sorting genetic algorithm from the heuristic search algorithm is used to solve the multi-objective optimization function. The algorithm population size is set to 100, and after 200 generations, a set of non-dominated solutions is obtained. From the non-dominated solution set, a solution is selected according to a preset engineering implementation priority. The engineering implementation priority stipulates prioritizing resonance avoidance. The selected solution corresponds to a step-by-step approach to the conflict area. The first step is to adjust the damping structure, and the second step is to reinforce the material with a thinned gradient layer on the adjusted structure. This approach serves as a compromise or step-by-step optimization scheme. It can be understood that the physical interference function... The calculation is based on the overlap area of ​​vector coordinates and the compatibility lookup results of material properties.

[0031] In practical implementation, all optimized enhancement measures are grouped and sorted according to their implementation areas on the fiber optic connector. The implementation areas are divided into the connector ferrule end face area, the connector housing middle area, and the tail sheath area. Measures within the same area are sorted according to their implementation order. For example, in the connector housing middle area, the order is: installing the damping structure followed by surface material cladding, forming a structured implementation plan list. It can be understood that the implementation plan list includes step numbers, implementation area coordinates, specific measure descriptions, and expected parameters. Optionally, the grouping basis can be the functional type of the measures, for example, grouping all frequency tuning-related measures together. In some embodiments, the structured implementation plan list is presented in tabular form, with columns including step numbers, three-dimensional coordinates, enhancement measure type, specific parameters, and reference drawing numbers. Optionally, for optimization schemes implemented in stages, different stage identifiers are used in the implementation plan list to indicate the manufacturing stage to which different steps belong. In practical implementation, the resulting implementation plan list is the final output of the composite performance enhancement strategy, which is a digital file that can be directly parsed by the subsequent instruction generation module.

[0032] In one embodiment of the invention, the instruction generation module parses enhancement measure types from a structured implementation plan list of composite performance enhancement strategies. The structured implementation plan list contains multiple entries, each specifying the enhancement measure type and its implementation location. For example, entry one requires the implementation of an additional damping structure at an axial coordinate of 10 mm to 12 mm on the housing, and entry two requires the implementation of local material reinforcement in a specific radial region of the housing. Based on the specific model information of the fiber optic connector, "LC-SM-APC," the instruction generation module calls the corresponding 3D part model library to obtain a 3D model of the "LC-SM-APC" model fiber optic connector. The 3D model is a digital file containing precise geometric dimensions and assembly relationships. In some embodiments, specific 3D coordinates of the implementation area are matched on the 3D model for each enhancement measure type. For an additional damping structure, the 3D coordinates of the implementation area are mapped to a set of closed boundary points on the surface of the 3D model by mapping the descriptive location "axial coordinate of the housing 10 mm to 12 mm." For local material reinforcement, the 3D coordinates of the implementation area are mapped to a specific curved surface region on the 3D model.

[0033] For reinforcement measures involving additional damping structures, the mass, stiffness, and installation interface dimensions of the implementation area in three-dimensional coordinates are calculated. The mass calculation is based on the volume of the implementation area and the density of the selected damping material (silicone rubber with a density of 1200 kg / m³). The volume of the implementation area is obtained as 15 m³ / mm³ through Boolean operations on the three-dimensional model, resulting in a calculated mass of 0.018 g. The stiffness calculation is based on the shear modulus of the damping material and the structural shape, with a shear modulus of 0.5 MPa. The installation interface dimensions are determined based on the boundary contour of the implementation area in three-dimensional coordinates, generating additional damping structure parameters containing a mass of 0.018 g, a shear modulus of 0.5 MPa, and a set of installation surface contour points. For reinforcement measures involving localized material reinforcement, the material composition gradient, cladding layer thickness, and bonding layer parameters of the implementation area in three-dimensional coordinates are calculated. The planning of the material composition gradient requires determining the target material performance curve for the target reinforcement area based on the yield strength and toughness requirements of the weak areas in the thermal stress deformation compensation scheme. In practical implementation, the target material performance curve specifies that the yield strength needs to linearly increase from 300 MPa to 450 MPa from the connector substrate to the reinforcing layer surface, and the fracture toughness needs to increase from 50 MPa·m1 / 2 to 80 MPa·m1 / 2. Based on the target material performance curve, stainless steel substrate material and Inconel alloy reinforcing material are selected from a pre-set material database, and their spatially continuous compositional variation path from the connector substrate to the reinforcing layer surface is planned to generate a material composition gradient. Refer to Table 1 for the specific planning of the material composition gradient.

[0034] Table 1: Material Composition Gradient Planning Table

[0035] Based on the thermal stress magnitude in the corresponding region obtained from thermodynamic simulation, the minimum material increment required to resist deformation is calculated, and the cladding layer thickness is determined considering process allowances. In one example, the formula for calculating the cladding layer thickness is:

[0036] in: Represents the thickness of the cladding layer. It is the difference between the maximum tensile stress obtained from thermodynamic simulation and the yield strength of the matrix. It is the stress concentration factor. It is the target yield strength of the reinforcing layer surface. It is a process allowance constant. Based on simulation data, It is 50 MPa. It is 1.2. It is 450 MPa. The calculated thickness of the cladding layer is 153 micrometers, rounded down to 155 micrometers, given a base thickness of 20 micrometers. This process allowance constant is understood to be determined based on the precision of the additive manufacturing process. Based on the compatibility data between the selected reinforcing material, Inconel nickel alloy, and the connector substrate material, stainless steel, the material composition and thickness of the intermediate transition layer are designed. Compatibility data indicates the need for a nickel-chromium transition layer to prevent cracking. The intermediate transition layer is composed of 50% stainless steel and 50% Inconel nickel alloy, with a designed thickness of 30 micrometers, serving as the bonding layer parameter. Optionally, the material composition gradient programming can use a nonlinear function, but a tabular format provides explicit discrete control points.

[0037] The additional damping structural parameters are integrated with the local material reinforcement scheme and associated with the corresponding coordinates on the 3D model. The additional damping structural parameters are associated with the coordinate point set "Set_A" on the surface of the 3D model, and the local material reinforcement scheme is associated with the surface patch "Face_B" of the 3D model, forming a detailed design instruction. In some embodiments, the detailed design instruction is a structured text file containing references to the 3D model, coordinate identifiers of each reinforcement region, and corresponding reinforcement parameter data blocks. It is understood that the format of the detailed design instruction is compatible with the input interface of the subsequent additive manufacturing module. Optionally, the detailed design instruction can be converted into a slice file and process parameter file specific to the additive manufacturing equipment. In a specific implementation, after the instruction generation module completes the generation of the detailed design instruction, it transmits it to the additive manufacturing module.

[0038] See Figure 4 This is a line graph showing the gradient distribution of material composition, clearly illustrating the variation of the proportions of the two materials from the substrate to the surface of the reinforcement layer in the local material reinforcement region of the prefabricated optical fiber connector in a smart substation, with depth. The composition exhibits a linear and continuous transition without abrupt changes, avoiding defects such as interface stress concentration and cracking caused by sudden changes in material composition. The proportions of the two materials intersect at a depth of 100 micrometers, with both stainless steel (substrate) and Inconel (reinforcement) accounting for 50% at the intersection. The material composition gradient distribution curve visually presents the continuous compositional change path from the stainless steel substrate to the Inconel reinforcement layer. This corresponds to the design of discrete control points for the material composition gradient in the local material reinforcement scheme, guiding the compositional control of material cladding during additive manufacturing and achieving a gradual transition of material properties from the connector substrate to the surface of the reinforcement layer.

[0039] In one embodiment of the present invention, the controller of the intelligent additive manufacturing device receives detailed design instructions from the instruction generation module. The detailed design instructions are a structured data file containing a structural printing path and material cladding parameters. The controller parses the detailed design instructions to separate structural printing path instructions and material cladding process parameter instructions. The structural printing path instructions include a spatial motion trajectory coordinate sequence for depositing the additional damping structure and the corresponding feed rate. The material cladding process parameter instructions include laser power, scanning speed, powder feeding rate, and material composition gradient control parameters. According to the structural printing path instructions, the printing nozzle is controlled to deposit material at a designated position on the fiber optic connector body according to a preset path. The designated position corresponds to a surface annular region with an axial coordinate of 10 mm to 12 mm on the fiber optic connector shell. The preset path is coaxial spiral deposition within this annular region with a layer thickness of 0.1 mm. The deposited material is a special silicone rubber material used to form the additional damping structure. Under the control of the material cladding process parameters, the fiber laser is activated as a high-energy beam source. According to the set material composition gradient, cladding layer thickness and bonding layer parameters, the reinforcing material is clad layer by layer in the designated area of ​​the fiber connector body. The designated area is the physical area on the shell corresponding to the three-dimensional model surface patch "Face_B". The laser power is set to 300 watts, the scanning speed is set to 5 millimeters per second, and the powder feeding rate is adjusted in real time according to the material composition gradient planning table to achieve continuous change of composition from the matrix to the reinforcing layer, so as to realize the metallurgical bonding between the reinforcing material and the stainless steel matrix.

[0040] During the structural printing and material cladding process, an integrated on-machine measurement system (IMMS) monitors the forming dimensions and molten pool status in real time. The IMS includes a laser scanning probe and a coaxially mounted infrared thermal imager. The laser scanning probe acquires real-time 3D point cloud data of the deposited silicone rubber damping structure layer or metal cladding layer, calculating the dimensional deviation from the corresponding 3D coordinates in the detailed design instructions. For example, the height deviation of the cladding layer in the Z direction is calculated to be +12 micrometers. The infrared thermal imager monitors the molten pool temperature field distribution and morphological characteristics in real time during the cladding process, extracting parameters such as molten pool width, depth, and tail-end cooling rate. The molten pool width is 0.8 mm, the molten pool depth is 0.15 mm, and the tail-end cooling rate is 1.2e4 Kelvin per second. These dimensional deviations, molten pool width, depth, and tail-end cooling rate parameters are input into a pre-trained process quality prediction model. This model is a neural network model trained based on historical manufacturing data, predicting the potential defect risk of the final formed part under the current parameters. In one example, the formula for calculating the potential defect risk value is:

[0041] in: Represents the potential defect risk value. It is the dimensional deviation in the Z direction. It is the actual measured width of the molten pool. It is the target molten pool width. These are material-related constants. It is the tail cooling rate. These are the model regression coefficients. If the predicted potential defect risk value... If the deviation exceeds the allowable threshold of 0.5, the process quality prediction model outputs adjustment suggestions, including reducing the laser power by 20 watts or increasing the scanning speed by 0.3 mm / s. These suggestions are fed back to the controller as real-time process adjustment signals. In some embodiments, the controller dynamically fine-tunes the printing or cladding parameters based on the real-time process adjustment signals, for example, adjusting the laser power from 300 watts to 280 watts in the next scan path. It is understood that the model regression coefficients are determined through fitting with a large amount of process experimental data.

[0042] See Figure 5 This is a resonance risk assessment and thermal stress deformation analysis diagram. It shows the distribution curves of resonance risk, thermal stress, and deformation at different locations on the fiber optic connector. The connector location is used as the horizontal axis, which includes five locations: the front end, middle, rear end, interface, and fixing point. The risk / stress level is used as the left vertical axis, and the deformation is used as the right vertical axis. Resonance risk and thermal stress reach their highest values ​​at the interface, and deformation reaches its lowest value at the rear end. The distribution curves of resonance risk, thermal stress, and deformation visually present the mechanical response characteristics of different locations on the fiber optic connector. Corresponding to the output results of the environmental stress modeling module and the risk simulation module, it is used to identify key locations on the fiber optic connector prone to resonance and thermal deformation, guiding the targeted design of resonance avoidance strategies and thermal stress deformation compensation schemes.

[0043] The above embodiments are only used to illustrate the technical methods of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical methods of the present invention without departing from the spirit and scope of the technical methods of the present invention.

Claims

1. A system for enhancing the mechanical properties of prefabricated optical fiber connectors in intelligent substations, characterized in that: include: The data acquisition module acquires the set of physical parameters of the prefabricated optical cable to be deployed in the smart substation and the set of expected service environment data. The set of physical parameters of the prefabricated optical cable includes the material property data, structural size data and natural frequency data of the optical fiber connector. The set of expected service environment data includes the electromagnetic vibration interference spectrum, temperature difference range data and mechanical stress application point information. The environmental stress modeling module extracts vibration spectrum features based on the electromagnetic vibration interference spectrum to obtain core vibration mode features, performs temperature stress analysis based on the temperature difference range data to obtain periodic thermal stress features, and constructs a comprehensive environmental stress model by combining the mechanical stress application point information. The risk simulation module inputs the natural frequency data of the fiber optic connector and the core vibration mode characteristics into the mode matcher to calculate the resonance risk and generate a resonance avoidance strategy. It also inputs the material property data and structural dimension data of the fiber optic connector and the periodic thermal stress characteristics into the thermodynamic simulator to analyze the connector deformation and generate a thermal stress deformation compensation scheme. The strategy fusion module integrates the resonance avoidance strategy and the thermal stress deformation compensation scheme to generate a composite performance enhancement strategy for the fiber optic connector.

2. The intelligent substation prefabricated optical cable fiber optic connector mechanical performance enhancement system according to claim 1, characterized in that, Based on the electromagnetic vibration interference spectrum, vibration spectrum features are extracted to obtain core vibration mode features, including: Bandpass filtering is performed on the electromagnetic vibration interference spectrum to separate the potential resonant frequency band related to the physical size of the optical fiber connector; Peak detection and mode decomposition are performed within the potential resonance frequency band to identify the dominant vibrational frequency components and their corresponding amplitude and phase information; Based on the dominant vibration frequency components, amplitude, and phase information, the vibration waveform that has the main impact on the optical fiber connector in the spatial dimension is reconstructed. Principal component analysis is performed on the reconstructed vibration waveform to extract the core waveform parameters that determine its dynamic response, and these core waveform parameters are used as the core vibration mode features.

3. The intelligent substation prefabricated optical cable fiber optic connector mechanical performance enhancement system according to claim 2, characterized in that, The inherent frequency data of the fiber optic connector and the core vibration mode characteristics are input into a mode matcher to calculate resonance risk and generate a resonance avoidance strategy, including: A frequency-response relationship matrix is ​​established in the modal matcher, wherein the frequency-response relationship matrix uses the order frequencies in the natural frequency data as the reference rows and the dominant vibration frequency components in the core vibration mode features as the reference columns. Calculate the modal superposition coefficient corresponding to each element in the frequency-response relationship matrix. The modal superposition coefficient represents the potential energy of resonance between the natural frequency of a connector of a specific order and a specific dominant vibration frequency component. Elements whose modal superposition coefficients exceed a preset safety threshold are selected to identify high-risk resonance frequency pairs; For each high-risk resonant frequency pair, the required frequency offset or damping increment is calculated to form a frequency tuning suggestion or additional damping configuration suggestion. All suggestions are then combined to form the resonant avoidance strategy.

4. The intelligent substation prefabricated optical cable fiber optic connector mechanical performance enhancement system according to claim 3, characterized in that, The material property data and structural dimension data of the fiber optic connector, along with the periodic thermal stress characteristics, are input into a thermodynamic simulator to perform connector deformation analysis, generating a thermal stress deformation compensation scheme, including: In the thermodynamic simulator, a three-dimensional geometric model of the fiber optic connector is established based on the structural dimension data; The coefficient of thermal expansion, elastic modulus, and yield strength parameters from the material property data are assigned to the three-dimensional geometric model; The periodic thermal stress characteristics are applied as boundary conditions to the surface of the three-dimensional geometric model and key connection parts, and transient thermal-structural coupling simulation calculations are performed. Obtain the stress distribution cloud map and cumulative plastic strain distribution of the optical fiber connector obtained from simulation calculations over a complete temperature cycle; By analyzing the stress distribution cloud map and cumulative plastic strain distribution, weak areas prone to yielding or fatigue failure are identified. For each of the weak regions, a pre-compensation geometry is designed or a gradient material transition zone is introduced. The pre-compensation geometry or gradient material transition zone is used to offset the deformation caused by thermal stress. All designs together constitute the thermal stress deformation compensation scheme.

5. The intelligent substation prefabricated optical cable fiber optic connector mechanical performance enhancement system according to claim 4, characterized in that, By integrating the resonance avoidance strategy and the thermal stress deformation compensation scheme, a composite performance enhancement strategy for the fiber optic connector is generated, including: A strategy fusion space is established, and the frequency tuning suggestions or additional damping configuration suggestions in the resonance avoidance strategy, as well as the pre-compensation geometry or gradient material transition zone design in the thermal stress deformation compensation scheme, are all mapped to multi-dimensional vectors in the strategy fusion space. Conflict detection is performed in the strategy fusion space to identify mutually restrictive enhancements in terms of spatial location or material properties; Cooperative optimization calculations are performed on conflicting enhancement measures to generate compromise or phased implementation optimization schemes, so that enhancement measures from different sources can coexist physically and complement each other functionally. All optimized enhancement measures are grouped and sorted according to their implementation area on the fiber optic connector to form a structured implementation plan list, which is the composite performance enhancement strategy.

6. The intelligent substation prefabricated optical cable fiber optic connector mechanical performance enhancement system according to claim 5, characterized in that, The method of performing collaborative optimization calculations on conflicting enhancement measures to generate compromise or phased implementation optimization schemes includes: Quantitatively assess the contribution weight of each enhancement measure involved in each conflict to the final mechanical performance enhancement target; Based on the contribution weights, a multi-objective optimization function is constructed, which aims to maximize the overall enhancement effect while minimizing the physical interference between measures. A heuristic search algorithm is used to solve the multi-objective optimization function, resulting in a set of non-dominated solutions. From the set of non-dominated solutions, a solution that balances implementation feasibility and enhancement effect is selected according to the preset engineering implementation priority, and is used as the optimized scheme for the compromise or phased implementation.

7. The intelligent substation prefabricated optical cable fiber optic connector mechanical performance enhancement system according to claim 6, characterized in that, Also includes: The instruction generation module, based on the composite performance enhancement strategy and combined with the specific model information of the optical fiber connector, generates detailed design instructions including additional damping structure parameters and local material enhancement schemes. The additive manufacturing module inputs the detailed design instructions into the intelligent additive manufacturing device, which then drives the device to perform structural printing and material cladding on the fiber optic connector body, forming a finished fiber optic connector with enhanced mechanical properties.

8. The intelligent substation prefabricated optical cable fiber optic connector mechanical performance enhancement system according to claim 7, characterized in that, Based on the aforementioned composite performance enhancement strategy, and combined with the specific model information of the fiber optic connector, detailed design instructions are generated, including additional damping structure parameters and local material reinforcement schemes, comprising: From the structured implementation plan list of the composite performance enhancement strategy, the types of enhancement measures that need to be implemented at specific locations on the fiber optic connector are analyzed; Based on the specific model information of the fiber optic connector, the corresponding 3D part model library is called to obtain the 3D model of the connector. On the three-dimensional model, specific three-dimensional coordinates of the implementation area are matched for each type of enhancement measure; For reinforcement measures of the additional damping structure type, calculate its mass, stiffness and installation interface dimensions in the three-dimensional coordinates of the implementation area, and generate the parameters of the additional damping structure; For reinforcement measures of the local material reinforcement type, the material composition gradient, cladding layer thickness and bonding layer parameters in the three-dimensional coordinates of the implementation area are calculated to generate the local material reinforcement scheme. The additional damping structure parameters are integrated with the local material reinforcement scheme and associated with the corresponding coordinates on the three-dimensional model to form the detailed design instructions; The reinforcement measures for localized material reinforcement types calculate the material composition gradient, cladding layer thickness, and bonding layer parameters in the three-dimensional coordinates of the implementation area, specifically including: Based on the yield strength and toughness requirements of the weak areas in the thermal stress deformation compensation scheme, determine the target material performance curve required for the target reinforcement area; Based on the target material performance curve, two or more base materials are selected from a pre-set material database, and their compositional variation path from the connector substrate to the surface of the reinforcing layer is planned in space to generate the material composition gradient. Based on the magnitude of thermal stress in the corresponding region obtained from thermodynamic simulation, the minimum material increment required to resist deformation is calculated, and the thickness of the cladding layer is determined by considering process allowance. Based on the compatibility data of the selected base material and the connector substrate material, the material composition and thickness of the intermediate transition layer are designed as parameters of the bonding layer.

9. The intelligent substation prefabricated optical cable fiber optic connector mechanical performance enhancement system according to claim 8, characterized in that, The detailed design instructions are input into the intelligent additive manufacturing device, which then performs structural printing and material cladding on the fiber optic connector body to form a finished fiber optic connector with enhanced mechanical properties, including: The controller of the intelligent additive manufacturing device parses the detailed design instructions and separates the structure printing path instructions and the material cladding process parameter instructions. According to the structural printing path instructions, the printing nozzle is controlled to deposit special polymer or metal material for forming additional damping structure at a designated position on the fiber optic connector body, following a preset path. Under the control of the material cladding process parameters, the high-energy beam source is activated, and according to the set material composition gradient, cladding layer thickness and bonding layer parameters, the reinforcing material is clad layer by layer in the designated area of ​​the optical fiber connector body to achieve metallurgical bonding with the substrate. During the structural printing and material cladding process, an integrated on-machine measurement system monitors the forming dimensions and molten pool status in real time, compares them with the expected values ​​in the detailed design instructions, and generates real-time process adjustment signals to feed back to the controller to dynamically fine-tune the printing or cladding parameters.

10. The intelligent substation prefabricated optical cable fiber optic connector mechanical performance enhancement system according to claim 9, characterized in that, During the structural printing and material cladding process, an integrated on-machine measurement system monitors the forming dimensions and molten pool state in real time, compares them with the expected values ​​in the detailed design instructions, and generates real-time process adjustment signals, including: The laser scanning probe acquires the three-dimensional point cloud data of the deposited structure or cladding layer in real time, and calculates the dimensional deviation between the data and the corresponding three-dimensional coordinates of the area in the detailed design instructions. The temperature field distribution and morphological characteristics of the molten pool during the cladding process are monitored in real time using an infrared thermal imager or a high-speed camera, and parameters such as the width, depth and tail cooling rate of the molten pool are extracted. The dimensional deviation, melt pool width, depth, and tail cooling rate parameters are input into a pre-trained process quality prediction model to predict the potential defect risk of the final formed part under the current parameters. If the predicted potential defect risk exceeds the allowable threshold, the process quality prediction model outputs adjustment suggestions, which include adjusting the printing speed, laser power, or wire feed speed. These adjustment suggestions serve as the real-time process adjustment signal.

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