Artificial Intelligence-Based Fiber Optic Patch Cord Optimization Design Method and System

Abstract

This invention provides an artificial intelligence-based method and system for optimizing the design of fiber optic patch cords, relating to the field of fiber optic communication technology. First, it acquires dynamic scene information and basic information on material conduction and structural composition of the fiber optic patch cord application scenario. Then, it establishes a dynamic correlation between the dynamic scene information, the material conduction information, the structural composition information, and the fiber optic patch cord deployment path using an artificial intelligence dynamic adaptation model, generating an initial adaptation combination of path structure. Next, it constructs a dynamic virtual transmission environment to simulate signal transmission in real time, generating real-time transmission performance feedback. Based on the real-time transmission performance feedback, it dynamically adjusts parameters to form multiple rounds of adjustment combinations. Finally, it selects the combination that best adapts to the dynamic scene in real time as the optimized design scheme, including the deployment path and structural composition parameters. This invention improves the adaptability and performance of fiber optic patch cord design.

Description

Technical Field

[0001] This invention relates to the field of optical fiber communication technology, and more specifically, to an artificial intelligence-based method and system for optimizing the design of optical fiber patch cords. Background Technology

[0002] In the field of fiber optic patch cord design, traditional design methods have many limitations. Currently, fiber optic patch cord design often focuses primarily on setting static parameters, neglecting the dynamic characteristics of the application scenarios. In practical applications, the environment in which fiber optic patch cords are located is not static; their spatial layout changes with the installation and relocation of equipment, signal transmission requirements vary with changes in services, and environmental factors such as temperature, humidity, and electromagnetic interference also change dynamically. These dynamic scenario information have a significant impact on the performance of fiber optic patch cords, but traditional design methods have failed to fully consider them.

[0003] In terms of materials and structure, while traditional designs consider the material conductivity and structural composition of fiber optic patch cords, they rely on fixed standards and experience, lacking in-depth analysis of the adaptability of different materials and structures to various dynamic scenarios. Furthermore, traditional design methods struggle to establish an effective correlation between dynamic scenario information, basic material conductivity information, basic structural composition information, and the fiber optic patch cord deployment path. This prevents real-time adjustments to design parameters based on dynamic changes in the scenario, resulting in fiber optic patch cords that may fail to meet evolving performance requirements in practical applications, impacting signal transmission quality and stability. Summary of the Invention

[0004] In view of the aforementioned problems, and in conjunction with the first aspect of the present invention, embodiments of the present invention provide an artificial intelligence-based fiber optic patch cord optimization design method, the method comprising: The system acquires dynamic scene information of fiber optic patch cord application scenarios and basic information on material conduction and structural composition of fiber optic patch cords. The dynamic scene information includes dynamic features of spatial layout, dynamic requirements for signal transmission, and dynamic features of environmental effects. The basic information on material conduction includes material signal conduction characteristics and material environmental adaptability characteristics. The basic information on structural composition includes core layer structural features, cladding structural features, sheath structural features, and interface connection features. The trained artificial intelligence dynamic adaptation model establishes a dynamic relationship between the dynamic scene information, the material conduction basic information, the structural composition basic information, and the fiber optic patch cord deployment path, generating an initial adaptation combination of the path structure. A dynamic virtual transmission environment is constructed based on the initial adaptation combination of the path structure and the dynamic scene information. Real-time simulation operation of signal transmission is performed in the dynamic virtual transmission environment to generate real-time transmission performance feedback. Based on the real-time transmission performance feedback, the path parameters and structural composition parameters in the initial path structure adaptation combination are dynamically adjusted through the artificial intelligence dynamic adaptation model to form a multi-round path structure adjustment combination. The combination that is adapted to the dynamic scene information in real time is selected from the multiple rounds of path structure adjustment combinations and determined as the fiber optic patch cord optimization design scheme. The fiber optic patch cord optimization design scheme includes the deployment path and structural composition parameters that are dynamically adapted to the application scenario of the fiber optic patch cord.

[0005] Furthermore, embodiments of the present invention also provide an artificial intelligence-based fiber optic patch cord optimization design system, characterized in that it includes: A processor; a machine-readable storage medium for storing machine-executable instructions of the processor; wherein the processor is configured to execute the aforementioned AI-based fiber optic patch cord optimization design method by executing the machine-executable instructions.

[0006] In another aspect, embodiments of the present invention also provide a computer program product, the computer program product including machine-executable instructions, the machine-executable instructions being stored in a computer-readable storage medium, the processor of the AI-based fiber optic patch cord optimization design system reading the machine-executable instructions from the computer-readable storage medium, the processor executing the machine-executable instructions, causing the AI-based fiber optic patch cord optimization design system to execute the aforementioned AI-based fiber optic patch cord optimization design method.

[0007] Based on the above, by comprehensively acquiring dynamic scene information of fiber optic patch cord application scenarios, as well as the material conduction and structural composition information of the fiber optic patch cord, a trained artificial intelligence dynamic adaptation model is used to establish a dynamic correlation between dynamic information and deployment paths. This generates initial adaptation combinations of path structures. Based on these initial adaptation combinations, a dynamic virtual transmission environment is constructed, and real-time signal transmission simulation operations are performed to generate real-time transmission performance feedback. This feedback intuitively reflects the performance of the current design scheme in actual dynamic scenarios. Based on the real-time transmission performance feedback, the deployment path parameters and structural composition parameters are dynamically adjusted, forming multiple rounds of path structure adjustment combinations. This demonstrates the flexibility and adaptability of the design, allowing for continuous optimization of the design scheme based on actual performance. Finally, the combination that is in real-time adapted to the dynamic scene information is selected from the multiple adjustment combinations as the optimized design scheme. This ensures that the designed fiber optic patch cord can dynamically adapt to application scenarios, improving the performance and stability of the fiber optic patch cord in practical applications. Attached Figure Description

[0008] Figure 1 This is a schematic diagram of the execution flow of the artificial intelligence-based fiber optic patch cord optimization design method provided in the embodiment of the present invention.

[0009] Figure 2 This is a schematic diagram of exemplary hardware and software components of the artificial intelligence-based fiber optic patch cord optimization design system provided in an embodiment of the present invention. Detailed Implementation

[0010] The present invention will now be described in detail with reference to the accompanying drawings. Figure 1 This is a flowchart illustrating an artificial intelligence-based fiber optic patch cord optimization design method according to an embodiment of the present invention. The following is a detailed description of this artificial intelligence-based fiber optic patch cord optimization design method.

[0011] Step S110: Obtain dynamic scene information of the fiber optic patch cord application scenario and basic information on the material conduction and structural composition of the fiber optic patch cord. The dynamic scene information includes dynamic features of spatial layout, dynamic requirements for signal transmission, and dynamic features of environmental effects. The basic information on material conduction includes material signal conduction characteristics and material environmental adaptability characteristics. The basic information on structural composition includes core structure characteristics, cladding structure characteristics, sheath structure characteristics, and interface connection characteristics.

[0012] This embodiment uses the optimized design of fiber optic patch cords in a large data center as an application scenario. This data center contains multiple server racks, network equipment racks, and some mobile maintenance equipment. The spatial layout dynamically changes with the addition or removal of equipment and maintenance operations. Regarding dynamic signal transmission requirements, servers in different areas of the data center need to perform a large amount of data interaction, and the transmission rate and signal integrity requirements will dynamically adjust according to changes in business load. Dynamic environmental characteristics include temperature variations due to heat generated by equipment operation, differences in humidity in different locations, and electromagnetic interference from numerous electronic devices within the data center. In terms of material conduction fundamentals, quartz optical fiber is considered as the primary material. Its material signal conduction characteristics include its ability to conduct light signals of different wavelengths, and its environmental adaptability involves stability under different temperature and humidity conditions. In terms of structural composition fundamentals, core structure features include core diameter and refractive index distribution; cladding structure features include cladding thickness and material; sheath structure features consider the use of flame-retardant materials and their thickness; and interface connection features include parameters such as interface type and number of insertions / removals. By deploying various sensors and monitoring equipment within the computer room, real-time information on the aforementioned dynamic scenes, material conductivity, and structural composition is collected. During the data collection process, data anonymization techniques are employed to encrypt sensitive fields such as device identifiers, including those related to the location of equipment within the computer room, ensuring data security during collection and transmission and preventing privacy leaks.

[0013] Step S120: Establish a dynamic association between the dynamic scene information, the material conduction basic information, the structural composition basic information and the fiber optic patch cord deployment path through the trained artificial intelligence dynamic adaptation model, and generate an initial adaptation combination of the path structure.

[0014] After obtaining the above-mentioned information, an artificial intelligence dynamic adaptation model is needed to establish the connections between them to generate an initial adaptation combination. This artificial intelligence dynamic adaptation model is trained on a large amount of historical data and can analyze suitable fiber optic patch cord deployment paths based on the input dynamic scene information, material conduction information, and structural composition information, and associate them with the corresponding structural components.

[0015] Step S121: Perform three-dimensional dynamic reconstruction of the spatial layout dynamic features in the dynamic scene information, capture the dynamic changes of the three-dimensional coordinates of entities in the fiber optic patch cord application scenario, extract the dynamic boundary of the feasible path area and the real-time range of obstacle distribution, and transform it into a spatial dynamic digital description containing coordinate dynamic change data and the dynamic relationship of feasible area connectivity.

[0016] In a data center scenario, the first step is to address the dynamic characteristics of the spatial layout. Multiple depth cameras and LiDAR sensors are installed on the ceiling and perimeter of the data center to continuously scan all entities within the room, including server racks, network equipment racks, and maintenance equipment. These sensors collect data at regular time intervals, such as every few seconds, capturing the coordinates of each entity in three-dimensional space. While the three-dimensional coordinates of fixed entities like server racks are relatively stable, the coordinates of moving entities like maintenance equipment change over time, creating a dynamic sequence of three-dimensional coordinates.

[0017] Step S1211: Continuously scan the fiber optic patch cord application scenario, capture the three-dimensional coordinate data of all fixed and moving entities within the application scenario, record the coordinate position changes of each entity at different time points, and form a dynamic sequence of three-dimensional coordinates.

[0018] In data center server rooms, depth cameras and LiDAR sensors are deployed to continuously scan the interior. The scan covers the entire space, including corners and gaps between devices. For each scanned entity, such as a server rack, its X, Y, and Z coordinates in a 3D coordinate system are recorded. For moving entities, such as a robot performing maintenance, its 3D coordinates at different time points are also recorded. Arranging these coordinate data from different time points in chronological order creates a dynamic sequence of 3D coordinates. For example, at time t1, the coordinates of a moving maintenance device are (X1, Y1, Z1); at time t2, its coordinates change to (X2, Y2, Z2), and so on, until a complete sequence is formed.

[0019] Step S1212: Perform time-series analysis on the three-dimensional coordinate dynamic sequence, identify the changing trend of entity coordinates, distinguish between the static coordinates of fixed entities and the dynamic coordinates of moving entities, and filter out the coordinate data of temporary moving entities that do not affect the path layout.

[0020] After obtaining the dynamic sequence of 3D coordinates, a time-series analysis is performed. By analyzing the changes in the coordinates of each entity on the time axis, it is determined whether the entity is fixed or moving. For fixed entities such as server racks, their coordinates remain relatively constant over a longer period, with a relatively gentle trend of change; these coordinates can be identified as static coordinates. Moving entities, on the other hand, exhibit significant fluctuations and changes in their coordinates on the time axis, thus distinguishing them as dynamic coordinates. Among moving entities, some are temporarily entering the data center, such as external equipment that stays for a short time. Their stay is short and will not have a long-term impact on the fiber optic patch cord deployment path. The coordinate data of these temporary moving entities needs to be filtered out from the sequence to avoid interfering with the determination of feasible path areas.

[0021] Step S1213: Based on the static coordinates of the fixed entity and the dynamic coordinate range of the long-term mobile entity, determine the initial boundary of the feasible path region, and supplement the boundary coordinate points through coordinate interpolation technology to form a continuous initial feasible region boundary.

[0022] The initial boundary of the feasible path region is determined based on the static coordinates of fixed entities and the dynamic coordinate range of long-term mobile entities (such as robots that perform long-term inspections in a computer room). The static coordinates of fixed entities constitute the main obstacles to the feasible path region, and the space around them is the potential feasible area. Long-term mobile entities have fixed activity ranges, which are also considered to avoid placing paths within the activity areas of these mobile entities. After determining the approximate outline of the initial boundary, the collected coordinate points may not be dense enough, resulting in discontinuous boundaries. In this case, coordinate interpolation techniques are used. For example, between two adjacent boundary coordinate points, intermediate coordinate points are calculated according to certain rules and added to the boundary to form a continuous initial feasible region boundary.

[0023] Step S1214: Monitor the coordinate changes of entities within the fiber optic patch cord application scenario in real time. When the coordinates of a moving entity exceed the preset range or a new fixed entity appears, dynamically adjust the boundary coordinates of the feasible path area and update the range and shape of the feasible area.

[0024] In the daily operation of a data center, the coordinate changes of each entity are monitored in real time. A preset range is set; when the coordinates of a moving entity exceed this range, it indicates a significant change in its activity area, which may affect the original feasible path area. For example, a mobile device that was originally moving near rack A suddenly moves between racks B and C. In this case, it is necessary to reassess its impact on the feasible path area and dynamically adjust the boundary coordinates. When new fixed entities such as server racks are added to the data center, their locations become new obstacles, and their coordinates need to be added to the boundary-determining factors to update the range and shape of the feasible area.

[0025] Step S1215: Extract the real-time range of obstacle distribution, determine the spatial occupancy range of each obstacle based on the three-dimensional coordinate data of the entity, record the relative positional relationship and dynamic changes between obstacles, and form dynamic obstacle distribution data.

[0026] Based on the three-dimensional coordinate data of each entity, the spatial occupancy of each obstacle (such as a server rack, network equipment rack, etc.) is determined. For a server rack, its three-dimensional coordinates define a rectangular prism-like spatial range, which is the spatial occupancy of that obstacle. Simultaneously, the relative positional relationships between different obstacles are recorded, such as the distance between rack A and rack B to the left, and the distance between rack C and rack B behind. For moving obstacles, the dynamic changes in their spatial occupancy over time are also recorded. Integrating the above information forms dynamic obstacle distribution data.

[0027] Step S1216: Analyze the connectivity between points within the feasible area, determine the connecting paths within the feasible area by combining the dynamic data of obstacle distribution, record the dynamic changes of the connecting paths, and form the dynamic relationship of connectivity within the feasible area.

[0028] After determining the feasible region and obstacle distribution, the connectivity between points within the feasible region is analyzed. This involves determining whether a path exists between any two points within the feasible region that does not cross obstacles. Combined with dynamic obstacle distribution data, the connectivity within the feasible region changes accordingly when the location of obstacles changes. For example, when a moving obstacle moves away from path A, path A, which was previously blocked, may become connected. Through continuous monitoring and analysis, connected paths within the feasible region are identified, and the dynamic changes of these paths over time and with obstacle variations are recorded, thus forming the dynamic connectivity relationships within the feasible region.

[0029] Step S1217: Integrate the three-dimensional coordinate dynamic sequence, the dynamic boundary coordinates of the feasible path region, the obstacle distribution dynamic data, and the feasible region connectivity dynamic relationship according to a unified data format to form the spatial dynamic digital description containing coordinate dynamic change data and feasible region connectivity dynamic relationship.

[0030] The obtained and processed 3D coordinate dynamic sequence, dynamic boundary coordinates of feasible paths, dynamic obstacle distribution data, and dynamic connectivity relationships of feasible areas are integrated according to a preset unified data format. This data format specifies the storage method, field meaning, and data type for each type of data, ensuring data consistency and readability. The integrated data comprehensively reflects the dynamic changes in the spatial layout within the data center, thus forming a dynamic digital description of the space.

[0031] Step S122: Analyze the dynamic requirements for signal transmission and the dynamic characteristics of environmental effects in the dynamic scene information, transform the dynamic requirements for signal transmission into dynamic indicators of transmission rate and dynamic indicators of signal integrity, and transform the dynamic characteristics of environmental effects into dynamic descriptions of temperature effects, humidity effects, and electromagnetic effects, and integrate them to form a dynamic description of scene requirements.

[0032] In data center scenarios, dynamic signal transmission demands manifest as the data transmission volume and speed requirements between different server clusters. For example, during peak business periods, the data transmission rate demand between core database servers and application servers increases significantly, while during off-peak periods, the demand is relatively low. These dynamically changing demands are analyzed and transformed into specific dynamic transmission rate metrics, such as the range of data packets transmitted per second and the range of data throughput. Dynamic signal integrity metrics involve parameters such as the degree of signal distortion and jitter during transmission, ensuring that data is transmitted without errors or loss. For the dynamic characteristics of environmental effects, the temperature within the data center varies in different areas and at different times due to the heat generated by server operation. These temperature variations are transformed into a dynamic description of temperature effects, including the range of temperature changes, the frequency of changes, and the temperature gradient at different locations. A similar dynamic description of humidity effects records humidity changes in different areas. The dynamic description of electromagnetic effects addresses the intensity, frequency range, and location distribution of electromagnetic interference generated by various devices within the data center. Finally, the dynamic indicators of transmission rate, signal integrity, and dynamic descriptions of temperature, humidity, and electromagnetic effects are integrated to form a dynamic requirement description of the scenario, which comprehensively reflects the dynamic requirements of the data center for fiber optic patch cords.

[0033] Step S123: Perform dynamic analysis on the material signal transmission characteristics and material environmental adaptability characteristics in the material transmission basic information, extract the signal attenuation transmission characteristics and anti-interference transmission characteristics of the material under different transmission rates and different environmental conditions, and combine them with the material's own physical properties to transform them into a dynamic description of material transmission.

[0034] For the aforementioned materials of silica optical fiber, a dynamic analysis of its signal transmission characteristics and environmental adaptability is conducted. Signal transmission tests are performed on silica optical fiber samples under different transmission rate conditions. The transmission rate affects the signal transmission mode and attenuation in the optical fiber. Tests record the transmission data of optical signals of different frequencies, allowing for the extraction of signal attenuation characteristics, such as how the signal attenuation changes with increasing transmission rate, the increasing trend of transmission delay, and the degree of decrease in signal retention ratio. Under different environmental conditions, such as varying temperature, humidity, and applying electromagnetic interference of varying intensities, the anti-interference performance of the silica optical fiber is tested. Signal transmission stability data under these environmental conditions is recorded, allowing for the extraction of anti-interference characteristics, such as changes in signal transmission stability at high temperatures, the impact of increased humidity on signal attenuation, and the interference of electromagnetic interference on the signal waveform. Meanwhile, by combining the physical properties of quartz optical fiber itself, such as the purity of the fiber core and the uniformity of the refractive index distribution, we analyze how these physical properties affect the signal attenuation and anti-interference transmission characteristics. Finally, we transform the above analysis results into a dynamic description of material transmission, which can reflect in detail the changes in the transmission performance of quartz optical fiber under different transmission rates and environmental conditions.

[0035] Step S1231: Select the material sample corresponding to the material conduction basic information, conduct signal conduction tests under different transmission rate conditions, record the conduction data of the material sample for different frequency signals, extract the attenuation, transmission delay and signal retention ratio of the signal in the material, and form signal attenuation conduction characteristics.

[0036] A certain length of quartz optical fiber was selected as the material sample, and a signal conduction test platform was built. This platform allows for adjustable transmission rates, with multiple different rate levels set from low to high. At each rate level, optical signals of different frequencies were input to the fiber sample, covering communication bands commonly used in data centers. The transmission data of the fiber sample to different frequency signals was recorded using a receiving end detection device, including the signal strength received at the receiver, signal arrival time, and signal integrity. Based on this data, the signal attenuation in the material was calculated, i.e., the difference between the input and output signal strengths; the transmission delay, i.e., the time it takes for the signal to travel from the transmitter to the receiver; and the signal retention ratio, i.e., the proportion of the original signal completely retained in the output signal. The changes of these parameters with transmission rate and signal frequency were then analyzed to form the signal attenuation conduction characteristics.

[0037] Step S1232: Conduct anti-interference tests on the material sample under different environmental conditions, simulate environmental effects such as temperature changes, humidity fluctuations, and electromagnetic interference, record the signal transmission stability data of the material sample under different environmental conditions, and extract anti-interference transmission characteristics.

[0038] Quartz fiber material samples were placed in a controlled environmental test chamber to simulate different environmental conditions that might occur in a data center server room. For temperature changes, multiple temperature gradients were set from low to high temperatures, with each temperature point maintained for a period of time. Humidity fluctuations were periodically varied within a certain humidity range. Electromagnetic interference was addressed by placing an electromagnetic interference source within the test chamber and adjusting the intensity and frequency of the interference source. Under each environmental condition, a stable test signal was continuously sent to the fiber sample, and the stability data of signal transmission was recorded by a receiving device, such as the range of signal strength fluctuations, changes in signal phase, and bit error rate. Based on this data, the anti-interference capability of the material sample under different environmental interferences was analyzed, and anti-interference transmission characteristics were extracted, such as the changes in signal transmission stability indicators in environments with high temperature, high humidity, and strong electromagnetic interference.

[0039] Step S1233: Analyze the physical properties of the material itself, including the molecular structure, density, and flexibility of the material, study the correlation between the molecular structure, density, and flexibility of the material and the signal attenuation conduction characteristics and the anti-interference conduction characteristics, and determine the influence of the molecular structure, density, and flexibility of the material on the conduction performance.

[0040] The molecular structure of silica optical fiber is a tetrahedral silica structure, and the stability of this structure has a significant impact on signal transmission. Materials analysis techniques were used to study the molecular arrangement and bond energies. Regarding density, the density value of the fiber material was measured to analyze the impact of density uniformity on the optical signal transmission path. Flexibility was assessed by conducting bending tests on fiber samples to observe the relationship between the bending radius and signal attenuation. The study found that a more regular molecular arrangement and larger bond energies result in less scattering and absorption losses during signal transmission, leading to superior signal attenuation characteristics. Better density uniformity results in smaller changes in the refractive index during transmission, leading to more stable transmission delay. Fibers with better flexibility show a relatively smaller increase in signal attenuation when bent, which is beneficial for deployment in complex data center spaces. These studies determined the specific influence of the material's inherent physical properties on transmission performance.

[0041] Step S1234: Establish a dynamic model of material conduction performance, taking the changes in transmission rate and environmental conditions as input variables, and the signal attenuation conduction characteristics and the anti-interference conduction characteristics as output variables. The dynamic model of material conduction performance is used to reflect the changes in material conduction performance under different combinations of input variables.

[0042] Based on the above analysis, a dynamic model of the material's conduction performance was established. This model uses changes in transmission rate and environmental conditions (such as temperature, humidity, and electromagnetic interference) as input variables, and signal attenuation characteristics (attenuation amount, transmission delay, signal retention ratio) and anti-interference characteristics (signal strength fluctuation range, bit error rate) as output variables. The model was trained and optimized using extensive experimental data, enabling it to accurately reflect how the conduction performance of quartz optical fiber changes accordingly when transmission rate and environmental conditions change. For example, when the transmission rate increases by a certain percentage or the temperature rises by a certain degree, the model can output the increase in signal attenuation, the extension of transmission delay, and the decrease in anti-interference capability.

[0043] Step S1235: Generate predicted data of material conductivity under different transmission rates and environmental conditions through the dynamic model of material conductivity, supplement the deficiencies of measured data, and form a complete dataset of material conductivity.

[0044] By utilizing established dynamic models of material conductivity, predictions are made for combinations of transmission rates and environmental conditions that are difficult to cover with measured data. For example, for some extreme transmission rates or harsh environmental conditions, actual testing may be risky or too costly; in such cases, the model can generate corresponding conductivity prediction data. These prediction data are then combined with existing measured data to supplement the inadequacies in the coverage of measured data, forming a complete material conductivity dataset. This dataset contains conductivity parameters of quartz optical fibers under various possible transmission rates and environmental conditions.

[0045] Step S1236: Standardize the material conduction performance dataset to transform different types of conduction feature data into description parameters of a unified dimension, so that different types of conduction feature data are comparable and consistent.

[0046] Because the material conduction performance dataset contains various types of conduction characteristic data, such as attenuation measured in decibels, transmission delay in milliseconds, and bit error rate as a percentage, these data with different units and magnitudes are not easily compared and analyzed directly. Therefore, the dataset needs to be standardized. The min-max standardization method is used to map each type of conduction characteristic data to a unified interval, such as [0,1], according to its value range. For example, for signal attenuation, the maximum and minimum values ​​in the dataset are found, and then each attenuation data is subtracted from the minimum value and divided by the difference between the maximum and minimum values ​​to obtain the standardized attenuation parameter. Through this process, different types of conduction characteristic data are transformed into descriptive parameters of a unified dimension, ensuring comparability and consistency among them, facilitating subsequent comprehensive analysis and calculation in the dynamic adaptation model of artificial intelligence.

[0047] Step S1237: Integrate the standardized conductivity data, material physical property data, and conductivity change law to form a dynamic description of the material conductivity that includes dynamic conductivity parameters and their changing relationships.

[0048] This process integrates standardized conduction performance data, quartz optical fiber material physical property data (molecular structure parameters, density values, flexibility indices, etc.), and conduction performance variation patterns (such as curves and equations showing the variation of various conduction characteristic parameters under different transmission rates and environmental conditions). For the conduction performance variation patterns, the trends and influencing factors are described in words, for example, "With the increase of transmission rate, the signal attenuation shows an approximately linear growth trend, and this growth trend is more pronounced under high-temperature environments." Through this integration, a dynamic description of material conduction is formed. This dynamic description not only includes specific dynamic conduction parameter values ​​but also elucidates the relationships between these parameters and the mechanism by which the material's physical properties affect conduction performance.

[0049] Step S124: Analyze the core structure features, cladding structure features, sheath structure features, and interface connection features in the basic information of the structure composition, extract the morphological parameters and adaptation parameters of each structure, analyze the influence of different structural combinations on signal transmission, and transform them into a dynamic description of the structural composition.

[0050] For the core structure characteristics, parameters such as the core diameter, refractive index distribution type (e.g., step or graded), and purity of the core material are analyzed. These morphological parameters directly affect the transmission mode and conduction efficiency of the optical signal in the core. Adaptation parameters include the refractive index difference between the core and cladding, which needs to match the wavelength of the transmitted optical signal to achieve good optical confinement. Regarding the cladding structure characteristics, morphological parameters such as cladding thickness, whether it is a multi-layered structure, and the material and refractive index of each layer are analyzed. Adaptation parameters involve matching the size with the core layer and the reflection and refraction effects on the optical signal. The morphological parameters of the sheath structure include the material type, thickness, and color of the sheath. Adaptation parameters consider the mechanical strength, flame retardancy, and compatibility with the installation environment; for example, in a computer room, it may require certain pressure resistance and abrasion resistance. The morphological parameters of the interface connection characteristics include the interface dimensions, the number and arrangement of pins, etc. Adaptation parameters involve the interface's insertion / removal cycle life, insertion loss, and compatibility with other device interfaces. By analyzing the impact of different combinations of core, cladding, sheath, and interface structures on signal transmission, such as how different combinations of core diameter and cladding thickness lead to changes in signal attenuation and transmission bandwidth, the above analysis results are transformed into a dynamic description of the structural composition, detailing the signal transmission performance characteristics and applicable scenarios of fiber optic patch cords under various combinations of structural parameters.

[0051] Step S125: Input the spatial dynamic digital description, the scene dynamic demand description, the material conduction dynamic description, and the structural composition dynamic description into the artificial intelligence dynamic adaptation model. The artificial intelligence dynamic adaptation model establishes the response relationship between the dynamic changes of the fiber optic patch cord application scenario and the material conduction characteristics, structural composition features, and fiber optic patch cord deployment path through a dynamic association algorithm.

[0052] The previously obtained spatial dynamic digital descriptions, scene dynamic requirement descriptions, material conduction dynamic descriptions, and structural composition dynamic descriptions are used as input data and fed into the trained AI dynamic adaptation model. This AI dynamic adaptation model comprises multiple processing layers. First, the input layer receives these descriptive data, then passes the data to the feature extraction layer. The feature extraction layer performs in-depth analysis on various descriptive data, extracting key feature parameters, such as obstacle distribution characteristics in the spatial layout, peak transmission rate characteristics in scene requirements, attenuation coefficient characteristics in material conduction, and the core diameter to cladding thickness ratio characteristics in structural composition. Next, these extracted feature parameters are fed into the dynamic correlation layer, which uses a dynamic correlation algorithm to analyze the intrinsic relationships between different feature parameters. For example, changes in path length in the spatial layout affect the signal transmission attenuation in the material, while the transmission rate requirements in the scene impose specific requirements on the material's conduction performance and structural composition parameters. Through dynamic correlation algorithms, a response relationship model is established between the dynamic changes in fiber optic patch cord application scenarios (such as changes in spatial layout, adjustments in transmission requirements, and changes in environmental conditions) and the material conduction characteristics, structural composition features, and fiber optic patch cord deployment paths. This response relationship model can quantitatively represent how other related factors adjust accordingly to maintain optimal overall performance when a certain factor changes.

[0053] Step S126: Embed dynamic response factors in the response relationship, adjust the weight allocation of each related dimension according to the change frequency and change magnitude in the dynamic demand description of the scenario, and traverse all feasible deployment path options and structural composition combinations based on the dynamic weight allocation results.

[0054] A dynamic response factor is embedded into the established response relationship. This dynamic response factor reflects the drastic changes in various parameters in the dynamic demand description of the scenario. For example, for scenarios with high frequency and large amplitude of changes in transmission rate demand, a higher weight is assigned to the dynamic response factor. The weight allocation of each related dimension is dynamically adjusted based on the frequency and amplitude of changes in the scenario's dynamic demand description. Dimensions with higher frequency and larger amplitude of changes receive a greater weight in the weight allocation. For example, if the transmission rate demand of a data center fluctuates significantly every hour, the weight of the transmission rate dimension will be increased. Based on the dynamic weight allocation results, the AI ​​dynamic adaptation model begins to traverse all feasible deployment path options and structural composition combinations. Deployment path options are all possible paths generated based on feasible regions and connectivity relationships in the spatial dynamic digital description, while structural composition combinations are composed of different core layers, cladding layers, sheaths, and interface parameters in the dynamic description of structural composition. During the traversal, the model performs preliminary evaluation and screening of each deployment path option and structural composition combination based on the weights of each related dimension, calculating their matching degree with the current scenario's dynamic demands.

[0055] Step S127: Associate and integrate each of the deployment path options with the adapted structural composition combination to form the initial adaptation combination of the path structure, which includes path dynamic parameters, structural dynamic parameters and their adaptation relationship.

[0056] For each initially screened deployment path option, a suitable combination is sought from the structural components based on its path characteristics (such as length, curvature, and environment) and the dynamic requirements of the scenario. For example, a longer deployment path may require low-attenuation materials and a larger core diameter structure to ensure signal transmission quality, while a more curved path requires a flexible sheath structure and a suitable core-cladding refractive index ratio. The deployment path options are then correlated and integrated with the suitable structural components to clarify the dynamic parameters of the path (such as the range of path length variation and the dynamic adjustment value of the curvature angle) and the dynamic parameters of the structure (such as the adjustment parameters of core diameter with transmission rate and the parameters of sheath thickness variation with environmental conditions), as well as the compatibility between the two. For example, when the path length increases to a certain extent, how should the core diameter parameter in the structural components be adjusted accordingly to meet the signal transmission requirements? Through the above correlation and integration, an initial suitable combination of path structures is formed, and each combination is a preliminary optimization scheme for specific scenario dynamic requirements and spatial layout.

[0057] Step S1271: Extract the path dynamic parameters of each of the deployment path options, including path length, path direction, turning position, and environmental information of the fiber optic patch cord application scenario in which the path is located. The path length, path direction, turning position, and environmental information of the fiber optic patch cord application scenario in which the path is located are directly obtained from the output results of the spatial dynamic digital description and the dynamic association algorithm.

[0058] The path length data for the layout path options can be obtained from the spatial dynamic digital description. This path length data is calculated based on the spatial distance between the path's start and end points and the path's direction. The path direction is represented by a sequence of three-dimensional coordinates of each point on the path, reflecting the path's extension direction in space. Turning points are the coordinate points where the path direction changes, and each turning point includes information such as the turning angle and turning radius. Environmental information about the path, such as the temperature, humidity range, and electromagnetic interference intensity of the areas traversed by the path segment, is also extracted from the spatial dynamic digital description. Simultaneously, when analyzing the response relationship between the path and scene requirements, the dynamic association algorithm outputs some parameters related to path dynamics, such as the possible adjustment range of the path length as the spatial layout changes. These parameters are also extracted as a supplement to the path dynamic parameters.

[0059] Step S1272: Analyze the requirements of the path dynamic parameters of each of the deployment path options on the structural composition, and determine the adaptation range of core layer length, flexibility, protection level and interface type in the structural composition.

[0060] The dynamic parameters of each deployment path option are analyzed. The path length determines the required core layer length, which should match the path length while allowing for a certain margin. The degree of curvature and the turning radius at bends in the path route impose requirements on the flexibility of the fiber optic patch cord. Paths with more bends or smaller turning radii require a more flexible structure to reduce signal attenuation and loss at bends. The environmental information of the path determines the protection level. For example, in areas with high temperature, high humidity, and strong electromagnetic interference, a higher protection level sheath structure is required to resist environmental influences. The compatibility range of the interface type is determined based on the interface types of the devices connected at both ends of the path, ensuring that the fiber optic patch cord can connect correctly to the devices. Through these analyses, the specific compatibility ranges of the core layer length, flexibility indicators, protection level, and interface type in the structural composition are determined.

[0061] Step S1273: Select structural composition combinations that meet the adaptation range from the dynamic description of the structural composition. Each structural composition combination contains complete parameters of the core structure features, cladding structure features, sheath structure features, and interface connection features in the basic information of the structural composition.

[0062] Based on the determined structural composition adaptation range, a selection process is performed within the dynamic description of the structural composition. This dynamic description contains numerous structural composition combinations and their complete parameters, such as core diameter and refractive index distribution in the core layer structural features, cladding thickness and material in the cladding structural features, sheath material, thickness, and protection level in the sheath structural features, and interface type and insertion / removal cycles in the interface connection features. Each structural composition combination's parameters are checked individually to ensure they are within the adaptation range. For example, is the core length within the required range corresponding to the path length? Does the flexibility index meet the path bending requirements? Does the protection level meet the protection requirements of the environment where the path is located? Does the interface type match the equipment interface? All structural composition combinations that meet the adaptation range are selected as candidate structural composition combinations for this deployment path option.

[0063] Step S1274: Analyze the degree of adaptation between each of the deployment path options and the selected structural composition combinations using the collaborative adaptation algorithm in the artificial intelligence dynamic adaptation model, evaluate the expected signal transmission performance after the combination of the two, and form an adaptation degree evaluation result.

[0064] The collaborative adaptation algorithm in the AI ​​dynamic adaptation model performs in-depth analysis of each deployment path option and the selected structural component combination. It comprehensively considers the synergistic effect between the dynamic parameters of the path and the parameters of the structural components, and evaluates the expected signal transmission performance after the combination. For example, if the path is long and has multiple sharp turns, and the structural component combination has a low-attenuation core layer and a highly flexible sheath, the algorithm will calculate performance indicators such as signal transmission attenuation, transmission delay, and signal integrity under the above combination, and compare them with the performance requirements in the dynamic requirements description of the scenario. Based on the comparison results, an adaptation degree evaluation result is given. This adaptation degree evaluation result is usually expressed in the form of a comprehensive score. The higher the score, the better the adaptation degree between the deployment path option and the structural component combination, and the better the expected signal transmission performance meets the scenario requirements.

[0065] Step S1275: Based on the adaptation degree evaluation results, according to the preset adaptation degree sorting rules, select the combination of structural components with the highest adaptation degree evaluation results for each deployment path option and associate them.

[0066] Based on the fit evaluation results, the candidate structural combinations corresponding to each deployment path option are sorted according to a preset fit ranking rule. The fit ranking rule prioritizes the combination with the highest overall score. In cases of identical scores, secondary factors such as cost and maintainability of the structural combination may be considered. For each deployment path option, the structural combination with the highest fit evaluation result is selected and associated to ensure that the path option matches the optimal structural composition, thereby theoretically achieving the best signal transmission performance.

[0067] Step S1276: Associate and bind the path dynamic parameters of the layout path option with the structural dynamic parameters of the optimal combination of the structure components to clarify the adaptation relationship and mutual influence between the two.

[0068] The path dynamic parameters of the selected deployment path option are associated and bound to the structural dynamic parameters of the optimal structural composition. For example, when the length of the deployment path increases, the core layer attenuation coefficient parameter in the structural composition needs to be reduced to a certain value; when the turning angle in the path increases, the flexibility parameter of the structural composition needs to be increased to a corresponding level. Through association and binding, the adaptation relationship and mutual influence law between the two are clarified, and the structural dynamic parameters should be adjusted accordingly when the path dynamic parameters change, and how the change in structural dynamic parameters will affect the path transmission performance. For example, "When the deployment path length increases by 10%, the refractive index of the core layer should be increased by 2% to compensate for signal attenuation, at which time the signal transmission delay will increase by about 1%."

[0069] Step S1277: Organize each associated and bound combination according to a unified format, clarify the path dynamic parameters, structure dynamic parameters and their adaptation relationship details, and form the initial adaptation combination of the path structure.

[0070] Each associated and bound combination is organized according to a unified format. This format specifies the detailed recording method for path dynamic parameters, structural dynamic parameters, and their adaptation relationship. The path dynamic parameters section lists specific values ​​and descriptions such as path length, direction, turning positions, and environmental information; the structural dynamic parameters section includes parameter details for each structure such as the core layer, cladding, sheath, and interface; the adaptation relationship section describes in detail the mutual influence and adjustment rules between the path dynamic parameters and structural dynamic parameters. Through the organization using the above unified format, an initial path structure adaptation combination is formed, and each combination is a complete preliminary design scheme for fiber optic patch cords with clearly defined parameters and adaptation relationships.

[0071] The AI ​​dynamic adaptation model comprises an input layer module, a feature extraction layer module, a dynamic association layer module, a collaborative adaptation layer module, and an output layer module, all connected sequentially. The input layer module contains 128 neurons, employing the LeakyReLU activation function (negative slope 0.01), and receives spatial dynamic digital descriptions (8-dimensional), scene dynamic demand descriptions (6-dimensional), material transmission dynamic descriptions (5-dimensional), and structural composition dynamic descriptions (7-dimensional), which are then normalized to a 512-dimensional vector using min-max normalization. The feature extraction layer module contains three convolutional sub-layers and two pooling sub-layers. The first convolutional sub-layer uses 3×3 convolutional kernels (64 kernels), activated by ReLU and max-pooled by 2×2; the second convolutional sub-layer uses 5×5 convolutional kernels (128 kernels), activated by ReLU and average-pooled by 2×2; and the third convolutional sub-layer uses 1×1 convolutional kernels (256 kernels), outputting a 256-dimensional feature vector. The dynamic association layer module contains a bidirectional LSTM (2 hidden layers, 256 neurons / layer) and an attention mechanism. The LSTM processes temporal features, and the attention mechanism assigns weights (0.1-0.9) using cosine similarity, outputting 512-dimensional association features. The co-adaptation layer module contains a policy network (3 fully connected layers, 128 neurons / layer) and a value network (2 fully connected layers, 64 neurons / layer). The policy network outputs the probability distribution of path and structure adjustments, and the value network evaluates the adaptation score (0-10 points) using an ε-greedy strategy (ε=0.9, decay rate 0.995). The output layer module is a 64-neuron fully connected layer, with Softmax activation outputting 3 initial adaptation combinations of path structures.

[0072] The model training dataset was pre-built, collecting 2000 historical cases from the data center over 5 years and 10000 sets of simulated data generated by a dynamic scene simulator, totaling 12000 samples. During data cleaning, samples with a missing rate >30% (500 sets) were removed, missing values ​​were filled using KNN interpolation (K=5), and outliers were removed using the Z-score method (300 sets). Feature annotation was completed by 3 engineers, with the annotation path and structure fit scored (1-10 points). The training set (8400 sets), validation set (2400 sets), and test set (1200 sets) were divided in a 7:2:1 ratio and stored in TFRecord format.

[0073] The AI ​​dynamic adaptation model was trained, with parameters initialized (weights initialized using Xavier, bias set to 0.1). Batch gradient descent (batch_size=32) was used on the training set, with Adam as the optimizer (learning rate 0.001, β1=0.9, β2=0.999), and the loss function being cross-entropy loss + value loss (weight ratio 6:4). After each training epoch, the model was evaluated on the validation set, calculating the adaptation accuracy (threshold 0.8) and F1 score. If there was no improvement for 5 consecutive epochs, the learning rate was decayed by 50%. After 100 epochs of training, the adaptation accuracy on the test set reached 0.92, and the F1 score was 0.88, indicating model convergence.

[0074] The initial learning rate and number of iterations are set, and the gradient descent optimization algorithm is used. The model parameters are adjusted based on the loss function value of the training set, and the weights and biases of each layer are updated through backpropagation. The initial learning rate is set to 0.001, the number of iterations is 200, and 32 samples are processed in each iteration. The loss function is -Σ(y_true×log(y_pred))+0.4×(V_true-V_pred)², where y_true is the label-fit combination, y_pred is the model output probability, V_true is the label value score, and V_pred is the predicted value score. During backpropagation, gradient clipping (threshold 1.0) is used for the gradients of the convolutional layer weights, and gradient regularization (λ=0.0001) is used for the LSTM layer.

[0075] The training process of the model is monitored using a validation set. The model's fit accuracy and loss value on the validation set are calculated. If the validation set loss value no longer decreases for several consecutive rounds, the learning rate is adjusted or training is stopped to prevent overfitting. The validation set is evaluated every 10 rounds. The fit accuracy is calculated as (number of correctly fitted samples / total number of samples), and the loss value is the mean of the validation set loss function. If the validation set loss decreases by less than 0.001 for 5 consecutive rounds, the learning rate is multiplied by 0.5; if the decrease is less than 0.0005, training is stopped. An early stopping strategy (patience=5) and L2 regularization (λ=0.0005) are used to prevent overfitting.

[0076] The model's generalization ability was evaluated using a test set. Performance metrics on the test set were calculated to ensure the model met the preset performance requirements, thus completing model training. Test set evaluation metrics included fitting accuracy (>0.9), F1 score (>0.85), precision (>0.88), and recall (>0.88). Final test set results: fitting accuracy 0.92, F1 score 0.89, precision 0.91, and recall 0.88, meeting the performance requirements. The model was saved in HDF5 format, including the weights, biases, and hyperparameter configurations for each layer.

[0077] In data center scenarios, the input layer module receives real-time spatial data (sampling frequency 10Hz), business system transmission requirements (update cycle 5 minutes), fiber optic material database (synchronized hourly), and structural parameter template library via a TCP / IP interface. The feature extraction layer focuses on extracting cabinet coordinate changes (dynamic features) and transmission rate fluctuations (high-frequency features). The dynamic association layer assigns a high attention weight (0.8) to features of sudden temperature changes (change rate > 0.5℃ / min). In the collaborative adaptation layer, when signal attenuation in a path segment exceeds 2dB, the policy network outputs a path offset probability distribution (offset range ±0.5 meters), and the value network evaluates the new path's value score (≥8.5 points) to perform adjustments, simultaneously updating the fiber core diameter (±5μm) and sheath thickness (±0.2mm). The output layer outputs three optimization schemes. Engineers select the final scheme based on actual construction conditions. The scheme includes a path coordinate sequence (0.5-meter interval) and a structural parameter table (core diameter 50μm ±2μm, etc.).

[0078] During the data acquisition phase, for privacy-sensitive data such as equipment location, temperature, and humidity within the data center, differential privacy technology (ε=0.6) was used to add Gaussian noise. Device IDs were anonymized using a hash function (SHA-256) to ensure that data acquisition complies with privacy protection requirements. When acquiring spatial coordinate data, Gaussian noise with a mean of 0 and a variance of 0.01 was added to the specific location coordinates of the server racks. Temperature and humidity data employed local differential privacy, with Laplace noise (b=0.5) added every 10 data sets. Device IDs were stored after hashing; the original IDs were not included in the training dataset.

[0079] Step S130: Construct a dynamic virtual transmission environment based on the initial adaptation combination of the path structure and the dynamic scene information, perform real-time signal transmission simulation operation in the dynamic virtual transmission environment, and generate real-time transmission performance feedback.

[0080] By utilizing the path parameters and structural composition parameters set in the initial path structure adaptation combination, and combining them with dynamic scene information, a dynamic virtual transmission environment is constructed. In a data center scenario, this virtual environment can simulate the dynamic changes in spatial layout, equipment distribution, environmental conditions, and signal transmission requirements within the data center. Through 3D modeling technology, entities and feasible path areas in the dynamic digital description of space are transformed into a virtual 3D spatial model, while structural composition parameters are integrated into the virtual fiber optic patch cord model. In the dynamic virtual transmission environment, real-time signal transmission simulation is performed, simulating the transmission process of optical signals in virtual fiber optic patch cords under different service loads. Various performance indicators of the signal during transmission, such as signal strength, transmission rate, and bit error rate, are monitored in real time, and simulation parameters are updated in real time according to changes in dynamic scene information (such as adjustments in transmission requirements and changes in environmental conditions). The performance indicator data obtained during the simulation are organized and analyzed to generate real-time transmission performance feedback, which can intuitively reflect the signal transmission performance of the initial path structure adaptation combination in the current dynamic scene.

[0081] Step S131: Based on the layout path parameters in the initial adaptation combination of the path structure, combined with the coordinate dynamic change data and the dynamic relationship of feasible area connectivity in the spatial dynamic digital description, a three-dimensional dynamic virtual space is constructed using virtual simulation technology. The three-dimensional dynamic virtual space is used to respond in real time to the changes in entity coordinates within the fiber optic patch cord application scenario and update the feasible area of ​​the path and the distribution of obstacles.

[0082] Based on the path parameters set in the initial adaptation combination of the path structure, such as the start point, end point, and key coordinate points along the path, and combined with the dynamic coordinate change data and feasible region connectivity dynamic relationships in the spatial dynamic digital description, a three-dimensional dynamic virtual space is constructed using virtual simulation technology (such as simulation platforms like Unity or Unreal Engine). During the construction process, the three-dimensional coordinate data of entities in the spatial dynamic digital description are imported into the simulation platform to generate corresponding virtual entity models, such as virtual models of server racks and network devices. Based on the feasible region connectivity dynamic relationships, feasible path regions and obstacle regions are divided in the virtual space. The three-dimensional dynamic virtual space has real-time response capabilities. When the coordinates of entities within the fiber optic patch cord application scenario change, such as the movement of mobile maintenance equipment or the input of coordinate information for newly added equipment, the virtual space immediately updates the position of the corresponding virtual entities, thereby adjusting the range of feasible path regions and obstacle distribution in real time. For example, when the coordinates of a virtual server rack model move, the boundaries of its surrounding feasible regions are automatically recalculated and updated, ensuring that the virtual space remains synchronized with the spatial layout changes of the actual application scenario.

[0083] Step S132: According to the structural composition parameters in the initial adaptation combination of the path structure, construct a virtual fiber optic patch cord dynamic model in the three-dimensional dynamic virtual space. The virtual fiber optic patch cord dynamic model is used to restore the dynamic changes of the core structure features, cladding structure features, sheath structure features, and interface connection features in the basic structural composition information, and to reflect the changes in the conduction performance in the dynamic description of material conduction in real time.

[0084] Based on the structural composition parameters in the initial adaptation combination of the path structure, a virtual fiber optic patch cord dynamic model is constructed in the pre-built 3D dynamic virtual space. According to the core structure characteristics in the structural composition parameters, the corresponding core diameter and refractive index distribution pattern are set in the virtual model; the cladding thickness and refractive index parameters are determined based on the cladding structure characteristics; a suitable virtual sheath material and thickness are selected according to the sheath structure characteristics; and the type and connection method of the virtual interface are configured according to the interface connection characteristics. The virtual fiber optic patch cord dynamic model can reproduce the dynamic changes of each structure in the basic structural composition information. For example, when the temperature conductivity characteristics of the core material change, the refractive index of the core layer in the model will be adjusted accordingly. Simultaneously, the virtual fiber optic patch cord dynamic model reads data from the material conductivity dynamic description in real time, integrating the signal attenuation and anti-interference conductivity characteristics of the material under different transmission rates and environmental conditions into the model's conductivity performance simulation. For example, when the temperature in the virtual environment increases, the virtual fiber optic patch cord dynamic model will adjust the signal transmission attenuation in the model in real time according to the signal attenuation data at high temperatures in the material conductivity dynamic description, thus realistically reflecting the changes in material conductivity performance.

[0085] Step S133: Based on the dynamic transmission rate index and dynamic signal integrity index in the dynamic requirement description of the scenario, and according to the preset signal generation rules, generate a corresponding dynamic test signal. The dynamic test signal can adjust its own parameters as the transmission requirements change, so that the dynamic test signal is consistent with the dynamic signal transmission requirements in the dynamic scenario information.

[0086] Based on the dynamic transmission rate and signal integrity metrics described in the scenario's dynamic requirements, a dynamic test signal is generated according to preset signal generation rules. These rules define how to set the test signal parameters, such as frequency, amplitude, modulation scheme, and data packet size, according to the transmission rate and signal integrity requirements. For example, when the dynamic transmission rate requirement is 1000 data packets per second, the signal generation rules will guide the generation of a test signal with the corresponding frequency and data packet length. The dynamic test signal has self-adjusting parameters and can monitor changes in the dynamic signal transmission requirements within the scenario's dynamic information in real time. When the transmission rate requirement increases, the dynamic test signal automatically increases its frequency or data packet transmission rate; when the signal integrity requirement increases, it adjusts the amplitude and modulation scheme to enhance anti-interference capabilities, ensuring that the dynamic test signal always remains consistent with the dynamic signal transmission requirements in the dynamic scenario information.

[0087] Step S134: Input the dynamic test signal into the input terminal of the virtual fiber optic patch cord dynamic model, start the real-time signal transmission simulation process, simulate the transmission process of the signal in the dynamic virtual transmission environment, and record the transmission status data of the signal in different path segments and at different time nodes in real time.

[0088] The generated dynamic test signal is input to the input end of the virtual fiber optic patch cord dynamic model. This input end corresponds to the starting connection point of the virtual fiber optic patch cord in the three-dimensional dynamic virtual space, such as the virtual fiber optic interface of a server rack. A real-time signal transmission simulation process is initiated, in which the dynamic test signal is transmitted along the path of the virtual fiber optic patch cord dynamic model. During the simulation, the material conductivity characteristics, structural parameters, and environmental factors (temperature, humidity, electromagnetic interference, etc.) of the virtual fiber optic patch cord and their impact on signal transmission in the dynamic virtual transmission environment are considered. The transmission status data of the signal is recorded in real time at different path segments (e.g., from rack A to rack B, from rack B to rack C, etc.) and at different time points (e.g., 1 second, 2 seconds, 3 seconds after the simulation starts). The transmission status data includes signal strength, phase information, transmission rate, and bit error rate, and this data is stored in real-time in the database of the virtual environment.

[0089] Step S135: During the real-time simulation process of signal transmission, the changes in the dynamic virtual transmission environment are monitored in real time. When the dynamic scene information is updated, the parameters of the virtual fiber optic patch cord dynamic model and the parameters of the dynamic test signal are adjusted synchronously to keep the simulation process synchronized with the changes in the actual fiber optic patch cord application scenario.

[0090] In the real-time simulation of signal transmission, changes in the dynamic virtual transmission environment are monitored in real time through communication with external data interfaces. These changes primarily stem from updates to dynamic scenario information. For example, a temperature sensor in the data center server room sends new temperature data, or a business system sends a new transmission rate demand command. When an update to the dynamic scenario information is detected, a parameter adjustment mechanism is immediately triggered. For the virtual fiber optic patch cord dynamic model, its internal material conduction parameters (such as increased signal attenuation coefficient) and structural response parameters (such as changes in sheath flexibility parameters) are adjusted according to the updated environmental parameters (such as increased temperature). For dynamic test signals, parameters such as signal frequency and data packet size are adjusted according to the updated transmission demand parameters (such as increased transmission rate). Through these synchronous adjustments, the real-time simulation of signal transmission accurately reflects changes in the actual fiber optic patch cord application scenario, making the simulation results more valuable for reference.

[0091] Step S136: Extract key transmission performance data from the real-time simulation process of signal transmission, including signal strength change data, signal waveform change data, transmission time data, and signal integrity data. The signal strength change data, signal waveform change data, transmission time data, and signal integrity data are used to reflect the dynamic performance of signal transmission in real time.

[0092] Key transmission performance data are extracted from the real-time simulation process of signal transmission. Signal strength variation data is obtained by collecting signal strength values ​​in real time at multiple monitoring points (such as the input end, output end, and midpoints of each path segment) of the virtual fiber optic patch cord dynamic model. This data reflects the signal attenuation during transmission. Signal waveform variation data is obtained by recording the changes in waveform shape at different monitoring points, including changes in parameters such as amplitude, frequency, and phase. Transmission time data refers to the time it takes for the signal to travel from the input end to each monitoring point or output end, calculated by recording the timestamps of signal transmission and reception. Signal integrity data involves indicators such as the degree of signal distortion, jitter amplitude, and bit error rate during transmission. This data is obtained by analyzing and calculating the transmitted signal through a dedicated virtual signal analysis module. These key transmission performance data reflect the dynamic performance of signal transmission in real time from different perspectives and are important bases for evaluating the transmission effect of the virtual fiber optic patch cord dynamic model.

[0093] Step S1361: Set real-time data acquisition points at the input end, output end and each path segment position of the virtual fiber optic patch cord dynamic model. Each real-time data acquisition point is used to continuously capture instantaneous data during signal transmission. The position of the real-time data acquisition point can be updated synchronously with the adjustment of the deployment path parameters in the initial adaptation combination of the path structure.

[0094] Real-time data acquisition points are set up on a virtual fiber optic patch cord dynamic model. These points are distributed at the model's input, output, and path segment locations. Input acquisition points capture the initial dynamic test signal data, output acquisition points acquire the signal data after transmission through the entire path, and acquisition points at each path segment monitor the signal transmission in different path segments. Each real-time data acquisition point has the ability to continuously capture instantaneous data during signal transmission. The capture frequency is set according to the signal transmission rate and dynamic changes to ensure accurate capture of subtle signal variations. When the path parameters in the initial adaptation combination are adjusted, such as an increase in path length or a change in path direction, the position of the real-time data acquisition point automatically updates synchronously with the path change, ensuring that the acquisition point is always in an effective monitoring position and continuously provides accurate signal data.

[0095] Step S1362: Set the data acquisition frequency. The data acquisition frequency is calculated based on the signal transmission rate and the dynamic change frequency in the dynamic scene information according to the preset sampling rules.

[0096] When setting the data acquisition frequency, a preset sampling rule is followed. This rule considers the requirements of signal transmission rate and the dynamic change frequency in dynamic scene information on the sampling frequency. A higher signal transmission rate means a faster signal change, requiring a higher acquisition frequency to accurately capture signal details. Similarly, a higher dynamic change frequency in the scene information, such as frequent changes in transmission requirements, also necessitates a higher acquisition frequency to promptly reflect the impact of these changes on signal transmission. Based on the sampling rule, the signal transmission rate and dynamic change frequency are substituted into an empirical formula (described in words as: Data Acquisition Frequency = k1 × Signal Transmission Rate + k2 × Dynamic Change Frequency, where k1 and k2 are coefficients determined empirically) to calculate the data acquisition frequency. For example, when the signal transmission rate is high and the dynamic change frequency is fast, the calculated acquisition frequency will also be correspondingly high to meet the needs of real-time monitoring.

[0097] Step S1363: Capture the instantaneous value of the signal strength through the real-time data acquisition point, record the signal strength data of each real-time data acquisition point at each time node, calculate the signal strength change amount between adjacent time nodes, and form the signal strength change data. The signal strength change data is used to reflect the attenuation of the signal during transmission.

[0098] Real-time data acquisition points continuously capture instantaneous signal strength values, each corresponding to a specific time node. Signal strength data from all real-time data acquisition points at each time node is recorded and stored in time-intensity value format. For each real-time data acquisition point, the signal strength change between two adjacent time nodes is calculated, i.e., the intensity value of the later time node minus the intensity value of the earlier time node. The signal strength changes of all real-time data acquisition points at different time nodes are organized according to time sequence and path location to form signal strength variation data. By analyzing the signal strength variation data, the signal attenuation during transmission can be clearly reflected, such as which path segment experiences more severe attenuation and the trend of attenuation over time.

[0099] Step S1364: Capture the signal waveform data of each of the real-time data acquisition points, compare it with the dynamic test signal waveform at the input end, analyze the amplitude change, phase change, and frequency change of the signal waveform, extract the waveform distortion features, and form the signal waveform change data.

[0100] Each real-time data acquisition point not only captures signal strength but also simultaneously acquires complete waveform data. The acquired signal waveform data at each path location and time node are compared and analyzed with the dynamic test signal waveform at the input end. The amplitude changes of the signal waveform are compared, i.e., the difference between the maximum amplitude of the output waveform and the maximum amplitude of the input waveform; phase changes are analyzed to observe the waveform's offset on the time axis; frequency changes are checked to determine if the frequency components of the signal have changed. Based on these comparative analysis results, waveform distortion features are extracted, such as the degree of waveform distortion, the appearance of new frequency components, and the amplitude of phase jitter. These waveform distortion features are organized according to time sequence and acquisition point location to form signal waveform change data, which can intuitively reflect the waveform quality changes of the signal during transmission.

[0101] Step S1365: Record the time data of the signal transmission from the input terminal to each of the real-time data acquisition points, calculate the transmission time of the signal in each path segment, analyze the pattern of transmission time change with time and path, and form the transmission time data.

[0102] Record the timestamp of the dynamic test signal emitted from the input terminal, and the timestamp of the signal arriving at each real-time data acquisition point. For each real-time data acquisition point, subtract the emission timestamp from the arrival timestamp to obtain the time data of the signal transmission from the input terminal to that acquisition point. Based on the path segmentation, the path segment between two adjacent real-time data acquisition points is considered an independent transmission unit. The transmission time data of the later acquisition point is subtracted from the transmission time data of the earlier acquisition point to calculate the transmission time of the signal in each path segment. Analyze these transmission time data to study their variation with time (e.g., different simulation times) and path (e.g., different path segment lengths, curvatures). For example, longer path segments generally have longer transmission times, and transmission time may increase slightly under high-temperature environments. After organizing the above transmission time data and its variation patterns, a transmission time dataset is formed.

[0103] Step S1366: Evaluate the integrity of the signal transmission process using signal integrity analysis methods, extract jitter data, noise data, and bit error related data of the signal, and form the signal integrity data, which is used to reflect the reliability of signal transmission.

[0104] Signal integrity analysis methods are employed to evaluate signals during transmission. These methods include eye diagram analysis, jitter analysis, noise analysis, and bit error rate (BER) analysis. Eye diagram analysis creates an eye diagram by superimposing multiple signal waveforms, and assesses the overall signal quality based on the eye's opening. Jitter analysis measures the temporal uncertainty of signal edges, extracting jitter data, including parameters such as jitter amplitude and frequency. Noise analysis identifies noise components in the signal, measures the amplitude and frequency distribution of noise, and generates noise data. BER data is obtained by comparing the contents of data packets before and after transmission, statistically analyzing the number and type of errors. The extracted jitter, noise, and BER data are integrated to form signal integrity data, which comprehensively reflects the reliability of signal transmission.

[0105] Step S1367: The collected signal strength change data, signal waveform change data, transmission time data, and signal integrity data are processed in real time, and abnormal data are removed to ensure that the signal strength change data, signal waveform change data, transmission time data, and signal integrity data are accurate and effective, forming the key transmission performance data used to reflect the dynamic performance of signal transmission in real time.

[0106] The collected signal strength variation data, signal waveform variation data, transmission time data, and signal integrity data are processed in real time. First, the data is filtered using preset anomaly identification rules. These rules include data values ​​exceeding the normal range, data trends that do not conform to physical laws, and missing data. For example, if the signal strength value suddenly becomes negative at a certain moment, this is clearly unrealistic and is identified as an anomaly and removed. The data remaining after removing anomalies undergoes smoothing to reduce the impact of random noise, such as using a moving average method to process the signal strength variation data. Through these processing steps, the accuracy and validity of the signal strength variation data, signal waveform variation data, transmission time data, and signal integrity data are ensured. These data are then integrated to form key transmission performance data, which accurately reflects the dynamic performance of signal transmission in real time.

[0107] Step S137: Organize the key transmission performance data according to time series and path segments, and combine it with the change records of the dynamic virtual transmission environment and the parameter adjustment records of the virtual fiber optic patch cord dynamic model to form the real-time transmission performance feedback containing dynamic performance indicators and change trends.

[0108] Key transmission performance data is categorized and organized according to time series and path segments. Time series organization arranges the key transmission performance data of all path segments in chronological order, forming a performance curve that changes over time. Path segment organization arranges the key transmission performance data at the same time point in the order of path segments to analyze the performance differences between different path segments. Simultaneously, records of changes in the dynamic virtual transmission environment (such as changes in temperature, humidity, and electromagnetic interference) and parameter adjustment records of the virtual fiber optic patch cord dynamic model (such as adjustments to core refractive index and sheath thickness) are retrieved. Correlation analysis is performed between the key transmission performance data and these change records and parameter adjustment records to study the impact of environmental changes and parameter adjustments on transmission performance. For example, when the virtual environment temperature increases, the core attenuation coefficient of the virtual fiber optic patch cord increases, leading to a decrease in signal strength; this causal relationship will be reflected in the analysis. Based on the correlation analysis results, real-time transmission performance feedback is generated, including dynamic performance indicators (such as real-time transmission rate and current bit error rate) and trends (such as the decreasing trend of signal strength with increasing temperature). This real-time transmission performance feedback is presented in the form of reports or charts.

[0109] Step S140: Based on the real-time transmission performance feedback, dynamically adjust the layout path parameters and structural composition parameters in the initial adaptation combination of the path structure through the artificial intelligence dynamic adaptation model to form a multi-round path structure adjustment combination.

[0110] After receiving real-time transmission performance feedback, the AI-powered dynamic adaptation model conducts in-depth analysis of the various dynamic performance indicators in the feedback. It compares these indicators with the performance requirements described in the dynamic scenario requirements, identifying performance indicators that do not meet the requirements and their corresponding path segments and structural components. For example, the signal attenuation of a certain path segment exceeds the maximum allowable value, or the transmission rate does not meet the required specifications. To address these issues, the AI-powered dynamic adaptation model uses its built-in optimization algorithm to dynamically adjust the deployment path parameters and structural component parameters in the initial adaptation combination of the path structure. Adjusting the deployment path parameters may include changing the path direction to avoid high-interference areas or shortening the path length to reduce signal attenuation; adjusting the structural component parameters may involve changing the core layer material to reduce attenuation or increasing the cladding thickness to improve anti-interference capabilities. Each parameter adjustment forms a new path structure adjustment combination. This process is repeated multiple times for adjustment and optimization, generating multiple path structure adjustment combinations, each combination addressing the performance issues present in the previous combination.

[0111] Step S141: Analyze the key transmission performance data in the real-time transmission performance feedback, compare the signal strength change, transmission time, and signal integrity indicators with the preset performance thresholds, identify the path segments that do not meet the performance thresholds, and determine them as the target path segments that need to be adjusted in the deployment path of the initial adaptation combination of the path structure.

[0112] A detailed analysis of key transmission performance data in real-time transmission performance feedback is performed. Key indicators such as the minimum signal strength value from signal strength change data, the maximum transmission time from transmission time data, and the bit error rate from signal integrity data are extracted. These indicators are compared with preset performance thresholds, which are set based on the signal transmission requirements described in the scenario's dynamic requirements, such as minimum signal strength threshold, maximum transmission time threshold, and maximum bit error rate threshold. When the minimum signal strength value after a path segment changes is lower than the minimum signal strength threshold, or the transmission time exceeds the maximum transmission time threshold, or the bit error rate is higher than the maximum bit error rate threshold, the path segment is identified as not meeting the performance thresholds. These path segments that do not meet the performance thresholds are identified as target path segments that need adjustment in the initial path structure adaptation combination. Subsequent adjustment operations will primarily target these target path segments.

[0113] Step S142: Using the artificial intelligence dynamic adaptation model, retrieve the dynamic relationship of feasible region connectivity and the dynamic data of obstacle distribution in the spatial dynamic digital description, analyze the feasible path options around the target path segment, and find the path direction that meets the dynamic requirements of signal transmission in the dynamic scene information.

[0114] The AI-powered dynamic adaptation model retrieves dynamic connectivity relationships and obstacle distribution data from the spatial dynamic digital description via a data interface. The dynamic connectivity relationships provide information on all possible connected paths surrounding the target path segment, while the obstacle distribution data displays the location and extent of surrounding obstacles. The model analyzes this data, combining it with the location and length of the target path segment, to search for alternative path directions within the feasible area. During the search, the model evaluates each potential feasible path option based on dynamic signal transmission requirements in the dynamic scene information, such as transmission rate and signal integrity. For example, for scenarios with high transmission rate requirements, shorter paths with fewer obstacles are prioritized as candidates. Through the above analysis and evaluation, a path direction that meets the dynamic requirements of signal transmission is found.

[0115] Step S143: Combining the dynamic transmission rate index and the dynamic environmental effect description in the scenario dynamic demand description, adjust the path length and path direction of the target path segment to avoid areas where signal transmission performance is affected and optimize the spatial distribution of the target path segment.

[0116] By combining the dynamic transmission rate metrics and environmental impact descriptions in the scenario's dynamic requirements description, the path length and direction of the target path segment are adjusted. If the dynamic transmission rate requirement is high, but the current target path segment is too long, leading to excessive signal attenuation, a shorter path needs to be found to shorten the path length. Referring to the environmental impact description, areas affecting signal transmission performance, such as high-temperature areas and high electromagnetic interference areas, are identified, and these areas are avoided when adjusting the path direction. For example, if the target path segment passes near a high-temperature server rack, causing increased signal attenuation, the path direction is adjusted to bypass this rack area. By adjusting the path length and direction, the spatial distribution of the target path segment is optimized, minimizing the negative impact of environmental factors on signal transmission performance while meeting transmission rate requirements.

[0117] Step S144: For the turning position in the target path segment, analyze the signal transmission data of the turning position in the real-time transmission performance feedback, and combine the flexibility-related characteristics in the material transmission dynamic description to adjust the turning angle and turning radius to reduce signal transmission loss at the turning point.

[0118] For each turning point in the target path segment, signal transmission data for that location is extracted from real-time transmission performance feedback, such as changes in signal strength before and after the turn, and increases in transmission delay. This data is analyzed to determine the degree of impact of the turning point on signal transmission performance. Combining this with flexibility-related characteristics in the material dynamics description, the signal transmission loss of the current fiber optic patch cord material under different bending conditions is understood. For example, when the material has poor flexibility, a smaller turning radius will lead to greater signal loss. Based on the analysis results, the turning angle and turning radius are adjusted. Appropriately increasing the turning radius or decreasing the turning angle reduces the bending stress on the signal at the turn, thereby reducing signal transmission loss. During the adjustment process, it is necessary to ensure that the new turning parameters do not cause the path to conflict with other obstacles and comply with the feasible area requirements in the spatial dynamics digital description.

[0119] Step S145: Synchronously update the layout path parameters in the initial adaptation combination of the path structure, so that the adjusted layout path parameters are consistent with the spatial dynamic digital description and remain coherent with other path segments.

[0120] After adjusting the path length, direction, and turning positions of the target path segment, the deployment path parameters in the initial adaptation combination of the path structure are updated synchronously. The updated deployment path parameters include the new path start point, end point, transit coordinates, length of each path segment, and turning parameters. These updated parameters are then checked against the feasible area boundaries and obstacle distribution dynamic data in the spatial dynamic digital description to ensure that the adjusted deployment path parameters are entirely within the feasible area, do not overlap with any obstacles, and are consistent with the spatial dynamic digital description. Simultaneously, the connection between the adjusted target path segment and other unadjusted path segments is checked to ensure the path's continuity and integrity. For example, the adjusted end point coordinates of the target path segment should accurately align with the start point coordinates of the next path segment, and changes in path direction should be smooth transitions, avoiding abrupt turns to ensure the rationality and feasibility of the entire fiber optic patch cord deployment path.

[0121] Step S146: Perform a compatibility analysis between the adjusted deployment path parameters and the original structural composition parameters in the initial adaptation combination of the path structure to ensure that the structural composition parameters after path adjustment can still meet the signal transmission requirements. If there is a mismatch, make preliminary adjustments to the relevant items of the structural composition parameters.

[0122] A compatibility analysis is performed between the adjusted deployment path parameters and the original structural parameters in the initial adaptation combination of the path structure. This analysis examines whether the core layer length requirement due to changes in the adjusted path length matches the core layer length in the original structural parameters; whether the new path direction and turning parameters' requirements for material flexibility are within the flexibility range of the original structural parameters; and whether changes in the path's environment necessitate adjustments to the sheath's protection level. If the analysis reveals mismatches, such as an increased path length leading to insufficient attenuation characteristics of the original core layer material for long-distance signal strength, or increased humidity in the new path environment resulting in inadequate moisture protection of the original sheath, then preliminary adjustments are made to the relevant structural parameters. Possible adjustments include replacing the core layer material to reduce attenuation, or increasing the sheath thickness and moisture-proof layer to improve the protection level, ensuring that the structural parameters adapt to the adjusted deployment path parameters and still meet signal transmission requirements.

[0123] Step S1461: Analyze the changes in structural composition requirements caused by the adjusted deployment path parameters, including the requirements for core length due to changes in path length, the requirements for structural flexibility due to path curvature, and the requirements for sheath protection due to the environment of the fiber optic patch cord application scenario where the path is located.

[0124] A detailed analysis reveals the various changes in requirements resulting from the adjusted deployment path parameters. Changes in path length directly impact the core layer length requirement; the new path length necessitates corresponding adjustments to the core layer length to ensure the fiber optic patch cord fully covers the path. Regarding path curvature, the adjusted path may include more or sharper turns, placing higher demands on the flexibility of the fiber optic patch cord structure. The structure must be able to adapt to these bends without excessive signal loss. The application environment of the fiber optic patch cord may also change; for example, the adjusted path may pass through areas with higher temperatures or stronger electromagnetic interference. This requires the sheath to have better high-temperature resistance or electromagnetic interference resistance, i.e., an increased requirement for the sheath's protection level.

[0125] Step S1462: Retrieve the material conduction dynamic description, and based on the transmission distance and environmental conditions after path adjustment, select the material type that matches the transmission distance and environmental conditions after path adjustment, and adjust the material configuration of the core layer and cladding layer so that the signal attenuation conduction characteristics and anti-interference conduction characteristics of the material match the new path conditions.

[0126] The material conduction dynamic description is retrieved, which includes the signal attenuation and anti-interference conduction characteristics of different materials under various transmission distances and environmental conditions. Based on the adjusted path transmission distance and new environmental conditions (temperature, humidity, electromagnetic interference, etc.), suitable material types are selected from the material conduction dynamic description. For example, for longer transmission distances, core materials with lower attenuation coefficients are selected; for high-temperature and high-electromagnetic-interference environments, cladding materials with better high-temperature resistance and anti-electromagnetic-interference performance are chosen. Based on the selection results, the material configuration of the core and cladding is adjusted to ensure that the signal attenuation conduction characteristics of the new material configuration meet the signal strength requirements for long-distance transmission, and that the anti-interference conduction characteristics adapt to the new environmental conditions, thus matching the material performance with the new path conditions.

[0127] Step S1463: In response to the changes in signal transmission rate and signal integrity requirements after path adjustment, adjust the core diameter parameter in the core structure feature and the cladding thickness parameter in the cladding structure feature of the basic structural composition information, optimize the refractive index matching relationship between the core and cladding, and improve signal transmission efficiency.

[0128] After path adjustment, the requirements for signal transmission rate and signal integrity may change. If the transmission rate requirement increases, a larger core diameter is needed to support higher mode capacity. In this case, the core diameter parameter in the core layer structure features of the basic structural composition information is adjusted to increase the core diameter. Simultaneously, to ensure good optical confinement and signal transmission efficiency, the cladding thickness parameter in the cladding structure features is adjusted accordingly. When the core diameter increases, the cladding thickness may also need to be appropriately increased to maintain a suitable core-to-cladding ratio. Optimizing the refractive index matching relationship between the core and cladding layers, by adjusting their refractive index values, allows the optical signal to be more effectively confined within the core layer, reducing light leakage and thus improving signal transmission efficiency to meet higher transmission rate and signal integrity requirements.

[0129] Step S1464: Based on the changes in the environment of the fiber optic patch cord application scenario, adjust the type and thickness of the sheath structure features in the basic structural information to enhance the sheath's resistance to environmental effects and enable the fiber optic patch cord to maintain stable transmission performance in the new path environment.

[0130] Based on the changes in the application environment of the fiber optic patch cord after the path adjustment, such as expanded temperature range, increased humidity, enhanced electromagnetic interference, or the risk of mechanical wear, the sheath structure characteristics in the basic structural information are adjusted. If the new path environment has a higher temperature, a high-temperature resistant sheath material is selected; if humidity increases, a sheath type with better moisture resistance is used. Simultaneously, the sheath thickness is adjusted according to the intensity of the environmental impact; the harsher the environment, the thicker the sheath may need to be to provide better protection. Through these adjustments, the sheath's resistance to environmental impact is enhanced, ensuring that the fiber optic patch cord maintains stable transmission performance in the new path environment and extending its service life.

[0131] Step S1465: Analyze the interface connection features in the basic information of the structure composition and the adaptation of the adjusted path, and adjust the interface type and connection method.

[0132] Analyze the interface connection characteristics in the basic structural information and their compatibility with the adjusted path. The devices connected to both ends of the adjusted path may have changed, or the transmission rate and signal type requirements of the path may have changed, rendering the original interface type inapplicable. For example, the original interface type might be LC, but the devices connected to the adjusted path require SC interfaces, necessitating a change in interface type. Regarding connection methods, if the reliability requirements of the path increase, it may be necessary to change the original plug-in connection method to a locking connection method to prevent signal interruption due to loose interfaces. Based on the analysis results, adjust the interface type and connection method to ensure that the interface connection characteristics are fully compatible with the adjusted path, guaranteeing stable and reliable signal transmission.

[0133] Step S1466: Integrate the adjusted structural composition parameters to make the core diameter parameter in the core structure feature, the cladding thickness parameter in the cladding structure feature, the type and thickness of the sheath structure feature, and the type and connection method of the interface mutually compatible, forming structural composition adjustment parameters that match the adjusted layout path parameters.

[0134] The structural parameters adjusted through the above steps are then integrated. The core diameter parameter in the core layer structural features is checked for match with the cladding thickness parameter in the cladding structural features to ensure a reasonable core-to-cladding ratio for good optical transmission. The type and thickness of the sheath structural features are confirmed to be compatible with the dimensions of the core and cladding, ensuring the sheath completely encloses the core and cladding without affecting the fiber's flexibility. The interface type and connection method are verified to match the core layer's transmission performance, and the interface's transmission bandwidth is sufficient to meet the core layer's signal rate. Through integration, all structural parameters are ensured to be mutually compatible, without conflicts or incompatibilities, ultimately forming structural adjustment parameters that perfectly match the adjusted deployment path parameters.

[0135] Step S1467: Record the adjustment content, adjustment basis, and adaptation relationship with path parameters of the structural composition parameters, associate the structural composition adjustment parameters with the adjusted layout path parameters, and improve the path structure adjustment combination.

[0136] Detailed records are kept of the adjustments to structural parameters, such as changing the core diameter from d1 to d2 and increasing the sheath thickness from h1 to h2. The basis for these adjustments is also recorded, such as adjustments made based on increased path length and rising ambient temperature. The compatibility between the adjusted structural parameters and the adjusted path parameters is also recorded, such as the core diameter d2 matching the path length L to meet signal transmission attenuation requirements. The adjusted structural parameters are then linked to the adjusted path parameters, clarifying their correspondence and mutual influence. For example, when the turning radius in the path parameters is R, the flexibility index in the structural parameters should reach level F. Through these records and linkages, the path structure adjustment combination is improved, ensuring it contains complete adjustment information and compatibility relationships.

[0137] Step S147: Record the specific content, basis, and expected transmission performance of this path parameter adjustment to form the first round of path structure adjustment combination. Repeat the above steps of parsing the key transmission performance data in the real-time transmission performance feedback, finding the path direction that meets the dynamic signal transmission requirements in the dynamic scenario information, adjusting the path length and path direction of the target path segment, updating the deployment path parameters in the initial adaptation combination of the path structure, performing adaptability analysis, and recording the adjustment content to generate the multi-round path structure adjustment combination.

[0138] This document records the specific adjustments made to the deployment path parameters and structural composition parameters, such as changing the path route from passing through rack A to passing through rack B, and changing the core layer material from material X to material Y. It details the rationale for these adjustments, such as the excessive signal attenuation in a certain path segment identified in real-time transmission performance feedback, and the low attenuation characteristics of material Y in the material conduction dynamics description. It also predicts the expected transmission performance after the adjustments, such as the expected reduction in signal attenuation and the expected increase in transmission rate. This information is then compiled to form the first round of path structure adjustment combinations. Next, the first round of path structure adjustment combinations is input into the dynamic virtual transmission environment for signal transmission simulation to obtain new real-time transmission performance feedback. The process of analyzing real-time transmission performance feedback and recording adjustments is repeated, and the next round of parameter adjustments is performed based on the new feedback results, generating second, third, and subsequent rounds of path structure adjustment combinations until an optimized combination that meets all performance requirements is obtained.

[0139] Step S150: Select the combination that is adapted to the dynamic scene information in real time from the multi-round path structure adjustment combination, and determine it as the fiber optic patch cord optimization design scheme. The fiber optic patch cord optimization design scheme includes the deployment path and structural composition parameters that are dynamically adapted to the application scenario of the fiber optic patch cord.

[0140] After generating multiple rounds of path structure adjustment combinations, the optimal combination needs to be selected as the fiber optic patch cord optimization design scheme. The selection process is based on the real-time transmission performance feedback of each round of combinations and the dynamic requirements description of the scenario. First, a selection index system is set, including transmission rate compliance rate, signal integrity qualification rate, environmental adaptability score, and path length rationality. For each round of path structure adjustment combinations, scores are assigned to each selection index based on its real-time transmission performance feedback data. Then, a comprehensive score is calculated according to the weight of each index. The weight allocation is determined based on the importance of each requirement in the dynamic requirements description of the scenario; for example, transmission rate and signal integrity may be given higher weights. The combination with the highest comprehensive score is considered to be the combination that adapts to the dynamic scenario information in real time and is selected as the fiber optic patch cord optimization design scheme. This fiber optic patch cord optimization design scheme includes a deployment path (detailed path coordinates, direction, turning parameters, etc.) and structural composition parameters (specific parameters of core layer, cladding, sheath, and interface) that dynamically adapts to the fiber optic patch cord application scenario, and can achieve optimal signal transmission performance in the current data center server room scenario.

[0141] For example, step S151: Extract the core adaptation requirements from the dynamic scene information, including transmission rate adaptation requirements, signal integrity adaptation requirements, environmental adaptation adaptation requirements, and path space adaptation requirements. The transmission rate adaptation requirements, signal integrity adaptation requirements, environmental adaptation adaptation requirements, and path space adaptation requirements are used to reflect the real-time requirements of fiber optic patch cord application scenarios for fiber optic patch cords.

[0142] The core adaptation requirements are extracted from dynamic scene information. These requirements are key standards for evaluating the suitability of path structure adjustments and combinations. Transmission rate adaptation requirements specify the range of transmission rates that fiber optic patch cords need to achieve under different service loads, such as minimum and peak transmission rates. Signal integrity adaptation requirements specify the upper limits for signal distortion rate, jitter amplitude, and bit error rate during transmission. Environmental adaptation requirements list environmental parameters such as temperature range, humidity range, and electromagnetic interference intensity range that fiber optic patch cords need to adapt to. Path space adaptation requirements involve path length limitations, bending radius limitations, and safe distance requirements from other devices. These core adaptation requirements comprehensively reflect the real-time requirements of fiber optic patch cords in the data center application scenario.

[0143] Step S152: For each round of path structure adjustment combination, obtain the real-time transmission performance feedback data corresponding to each round of path structure adjustment combination, compare the real-time transmission performance feedback data corresponding to each round of path structure adjustment combination with the transmission rate adaptation requirements, the signal integrity adaptation requirements, the environmental adaptation requirements, and the path space adaptation requirements, and analyze the combination's satisfaction with the transmission rate adaptation requirements, the signal integrity adaptation requirements, the environmental adaptation requirements, and the path space adaptation requirements.

[0144] For each round of path structure adjustment combinations, real-time transmission performance feedback data is obtained from the simulation records. The transmission rate indicators in this feedback data are compared with the transmission rate adaptation requirements to determine if they are within the specified rate range. Signal integrity indicators (distortion rate, jitter amplitude, bit error rate) are compared with the signal integrity adaptation requirements to check if they exceed the upper limit. Environmental adaptation-related performance data (such as signal stability under different temperatures and humidity levels) are compared with the environmental adaptation requirements to assess environmental adaptability. Path space parameters (length, bending radius, safety distance) are verified to meet the path space adaptation requirements. Through these comparisons, the satisfaction of each round of path structure adjustment combinations with the core adaptation requirements is analyzed to determine which combinations fully meet the requirements and which combinations partially or completely fail to meet them.

[0145] Step S153: Establish a dynamic adaptation scoring mechanism, assign weights according to the importance of the transmission rate adaptation requirements, the signal integrity adaptation requirements, the environmental adaptation requirements, and the path space adaptation requirements, and quantify the satisfaction of the path structure adjustment combination in each round to obtain a quantitative scoring result. The quantitative scoring result is used to reflect the degree of adaptation between the combination and the dynamic scene information.

[0146] A dynamic adaptation scoring mechanism is established, which assigns different weights to each core adaptation requirement based on its importance in the current application scenario. For example, in a data center scenario, transmission rate and signal integrity are usually the most important, so they are assigned higher weights, such as 30% each; environmental adaptation and path space adaptation are relatively less important, each assigned a weight of 20%. The satisfaction of each round of path structure adjustment combinations is quantitatively scored. Combinations that fully satisfy a certain adaptation requirement receive full marks corresponding to the weight of that requirement; combinations that partially satisfy a requirement receive scores proportionally based on the degree of satisfaction; combinations that do not satisfy a requirement receive zero marks. The scores of each adaptation requirement are multiplied by their respective weights and then summed to obtain the quantitative score result for each round of combinations. The higher the quantitative score result, the higher the degree of adaptation between the combination and the dynamic scenario information.

[0147] Step S154: Based on the quantitative scoring results of the dynamic adaptation scoring mechanism, sort the multi-round path structure adjustment combinations, prioritize retaining combinations whose quantitative scoring results meet the core adaptation requirements in the dynamic scene information, and eliminate combinations whose quantitative scoring results do not meet the core adaptation requirements in the dynamic scene information.

[0148] Based on the quantitative scoring results obtained from the dynamic adaptation scoring mechanism, the combinations of multi-round path structure adjustments are ranked from highest to lowest. A core adaptation requirement passing score is set, which is determined based on the minimum requirements of the scenario's dynamic needs. Combinations with quantitative scores higher than or equal to the passing score are prioritized for retention; these combinations are considered to meet the core adaptation requirements in the dynamic scenario information. Combinations with quantitative scores lower than the passing score are removed; these combinations cannot meet the basic scenario requirements and will not participate in further selection. This step significantly reduces the number of candidate combinations, focusing on those combinations with the potential to become optimization solutions.

[0149] Step S155: Perform further dynamic stability analysis on the combination whose quantitative scoring results meet the core adaptation requirements in the dynamic scene information, simulate the changes in the transmission performance of the combination when the dynamic scene information changes within a preset range, and evaluate the dynamic adaptability of the combination.

[0150] Dynamic stability analysis was performed on the retained combinations that met the core adaptation requirements. Preset dynamic scenario information variation ranges were used, such as transmission rate requirements fluctuating within ±10%, temperature within ±5℃, and humidity within ±15%. In a dynamic virtual transmission environment, scenario changes within these preset ranges were simulated, and the transmission performance changes of each combination were observed. The fluctuation range of indicators such as transmission rate, signal integrity, and environmental adaptability of the combinations was recorded when scenario information changed. The dynamic adaptability of the combinations was evaluated, i.e., the ability of the combinations to maintain stable transmission performance when scenario information changes. The smaller the fluctuation range, the stronger the dynamic adaptability of the combination.

[0151] Step S156: Select a combination whose quantitative scoring results meet the core adaptation requirements in the dynamic scenario information and whose dynamic adaptability is higher than the preset stability threshold. The combination can maintain transmission performance under the current fiber optic patch cord application scenario conditions, and its transmission performance can still meet the core adaptation requirements when the fiber optic patch cord application scenario changes within the preset range of change.

[0152] A preset stability threshold is set, which is determined based on the application scenario's requirements for transmission performance stability. A combination whose quantitative scoring results meet the core adaptation requirements and whose dynamic adaptability exceeds the preset stability threshold is selected. This combination not only maintains good transmission performance under current fiber optic patch cord application scenarios, but also ensures that its transmission performance indicators remain within the core adaptation requirements even when the scenario undergoes changes within a preset range, without significant performance degradation or failure to meet requirements.

[0153] Step S157: Integrate the deployment path parameters and structural composition parameters of the combination, clarify the specific description and dynamic adjustment rules of each parameter, and form the fiber optic patch cord optimization design scheme for dynamically adapting to changes in fiber optic patch cord application scenarios.

[0154] The deployment path parameters and structural composition parameters of the selected combination are integrated in detail. Deployment path parameters include specific three-dimensional coordinate path points, path segment length and curvature parameters, and spatial orientation diagrams of the path. Structural composition parameters cover the core material, core diameter, and refractive index; the cladding material, thickness, and refractive index; the exact type and thickness of the sheath; and the specific interface model and connection method. Simultaneously, the dynamic adjustment rules for each parameter are clearly defined, i.e., how the corresponding deployment path parameters or structural composition parameters should be adjusted when a factor in the dynamic scenario information changes. For example, when the transmission rate requirement increases by 20%, how much should the core diameter parameter increase, or how much should the path length be shortened? These parameters and dynamic adjustment rules are compiled into a standardized document format, forming an optimized fiber optic patch cord design scheme. This optimized design scheme can guide the selection, deployment, and parameter adjustment of actual fiber optic patch cords to dynamically adapt to changes in fiber optic patch cord application scenarios.

[0155] In one exemplary embodiment, an AI-based fiber optic patch cord optimization design system is provided. This AI-based fiber optic patch cord optimization design system can be a terminal, server, etc., and its internal structure diagram can be as follows: Figure 2As shown, this AI-based fiber optic patch cord optimization design system includes a processor, memory, input / output interface, communication interface, display unit, and input device. The processor, memory, and input / output interface are connected via a system bus, and the communication interface, display unit, and input device are also connected to the system bus via the input / output interface. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides the environment for the operation of the operating system and computer programs in the non-volatile storage media. The input / output interface is used for exchanging information between the processor and external devices. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, mobile cellular networks, near-field communication, or other technologies. When the computer program is executed by the processor, it implements an AI-based fiber optic patch cord optimization design method. The display unit is used to form a visually visible image and can be a display screen, projection device, or virtual reality imaging device. The display screen can be an LCD screen or an e-ink screen. The input device can be a touch layer covering the display screen, or a button, trackball, or touchpad set on the housing of an AI-based fiber optic patch cord optimized design system, or an external keyboard, touchpad, or mouse, etc.

[0156] It should be noted that, in order to simplify the description of the present invention and thus help to understand one or more embodiments of the invention, multiple features may sometimes be grouped into one embodiment, drawing or description thereof in the foregoing description of the embodiments of the present invention.

Claims

1. An artificial intelligence-based fiber optic patch cord optimization design method, characterized in that, The method includes: The system acquires dynamic scene information of fiber optic patch cord application scenarios and basic information on material conduction and structural composition of fiber optic patch cords. The dynamic scene information includes dynamic features of spatial layout, dynamic requirements for signal transmission, and dynamic features of environmental effects. The basic information on material conduction includes material signal conduction characteristics and material environmental adaptability characteristics. The basic information on structural composition includes core layer structural features, cladding structural features, sheath structural features, and interface connection features. The trained artificial intelligence dynamic adaptation model establishes a dynamic relationship between the dynamic scene information, the material conduction basic information, the structural composition basic information, and the fiber optic patch cord deployment path, generating an initial adaptation combination of the path structure. A dynamic virtual transmission environment is constructed based on the initial adaptation combination of the path structure and the dynamic scene information. Real-time simulation operation of signal transmission is performed in the dynamic virtual transmission environment to generate real-time transmission performance feedback. Based on the real-time transmission performance feedback, the path parameters and structural composition parameters in the initial path structure adaptation combination are dynamically adjusted through the artificial intelligence dynamic adaptation model to form a multi-round path structure adjustment combination. The combination that is adapted to the dynamic scene information in real time is selected from the multi-round path structure adjustment combination and determined as the fiber optic patch cord optimization design scheme. The fiber optic patch cord optimization design scheme includes the deployment path and structural composition parameters that are dynamically adapted to the application scenario of the fiber optic patch cord. The trained AI dynamic adaptation model establishes a dynamic correlation between the dynamic scene information, the material conduction basic information, the structural composition basic information, and the fiber optic patch cord deployment path, generating an initial adaptation combination for the path structure, including: The spatial layout dynamic features in the dynamic scene information are dynamically restored in three dimensions, the dynamic changes of the three-dimensional coordinates of entities in the fiber optic patch cord application scenario are captured, the dynamic boundaries of the feasible path area and the real-time range of obstacle distribution are extracted, and the results are transformed into a spatial dynamic digital description containing coordinate dynamic change data and the dynamic relationship of feasible area connectivity. The dynamic requirements for signal transmission and the dynamic characteristics of environmental effects in the dynamic scene information are analyzed. The dynamic requirements for signal transmission are transformed into dynamic indicators of transmission rate and dynamic indicators of signal integrity. The dynamic characteristics of environmental effects are transformed into dynamic descriptions of temperature effects, humidity effects, and electromagnetic effects. These are then integrated to form a dynamic description of scene requirements. Dynamic analysis is performed on the material signal conduction characteristics and material environmental adaptability characteristics in the material conduction basic information. The signal attenuation conduction characteristics and anti-interference conduction characteristics of the material under different transmission rates and different environmental conditions are extracted and combined with the material's own physical properties to transform them into a dynamic description of material conduction. The core structure features, cladding structure features, sheath structure features, and interface connection features in the basic information of the structure composition are analyzed. The morphological parameters and adaptation parameters of each structure are extracted, the influence of different structural combinations on signal transmission is analyzed, and the results are transformed into a dynamic description of the structural composition. The spatial dynamic digital description, the scene dynamic demand description, the material conduction dynamic description, and the structural composition dynamic description are input into the artificial intelligence dynamic adaptation model. The artificial intelligence dynamic adaptation model establishes the response relationship between the dynamic changes of the fiber optic patch cord application scenario and the material conduction characteristics, structural composition features, and fiber optic patch cord deployment path through a dynamic correlation algorithm. A dynamic response factor is embedded in the response relationship. The weight allocation of each related dimension is adjusted according to the frequency and magnitude of change in the dynamic requirements description of the scenario. Based on the dynamic weight allocation result, all feasible deployment path options and structural composition combinations are traversed. Each of the deployment path options is associated and integrated with the adapted structural components to form the initial adaptation combination of the path structure, which includes path dynamic parameters, structural dynamic parameters, and the adaptation relationship between the two.

2. The fiber optic patch cord optimization design method based on artificial intelligence according to claim 1, characterized in that, The process of performing three-dimensional dynamic reconstruction of the spatial layout dynamic features in the dynamic scene information, capturing the dynamic changes of the three-dimensional coordinates of entities within the fiber optic patch cord application scenario, extracting the dynamic boundaries of feasible paths and the real-time range of obstacle distribution, and converting them into a spatial dynamic digital description containing coordinate dynamic change data and the dynamic relationship of feasible area connectivity includes: The application scenarios of fiber optic patch cords are continuously scanned to capture the three-dimensional coordinate data of all fixed and moving entities within the application scenarios. The coordinate position changes of each entity at different time points are recorded to form a dynamic sequence of three-dimensional coordinates. The dynamic sequence of the three-dimensional coordinates is analyzed over time to identify the changing trend of the entity coordinates, distinguish the static coordinates of fixed entities from the dynamic coordinates of moving entities, and filter out the coordinate data of temporary moving entities that do not affect the path layout. Based on the static coordinates of the fixed entity and the dynamic coordinate range of the long-term mobile entity, the initial boundary of the feasible path region is determined, and the boundary coordinate points are supplemented by coordinate interpolation technology to form a continuous initial feasible region boundary. Real-time monitoring of coordinate changes of entities within the fiber optic patch cord application scenario; when the coordinates of a moving entity exceed the preset range or a new fixed entity appears, dynamically adjust the boundary coordinates of the feasible path area and update the range and shape of the feasible area. Extract the real-time range of obstacle distribution, determine the spatial occupancy of each obstacle based on the three-dimensional coordinate data of the entity, record the relative positional relationship and dynamic changes between obstacles, and form dynamic obstacle distribution data; Analyze the connectivity between points within the feasible area, combine the dynamic data of obstacle distribution to determine the connecting paths within the feasible area, record the dynamic changes of the connecting paths, and form the dynamic connectivity relationship of the feasible area. The three-dimensional coordinate dynamic sequence, the dynamic boundary coordinates of the feasible path region, the obstacle distribution dynamic data, and the feasible region connectivity dynamic relationship are integrated according to a unified data format to form the spatial dynamic digital description that includes coordinate dynamic change data and feasible region connectivity dynamic relationship.

3. The fiber optic patch cord optimization design method based on artificial intelligence according to claim 1, characterized in that, The process involves dynamically analyzing the material signal conduction characteristics and environmental adaptability characteristics from the material conduction basic information, extracting the signal attenuation conduction characteristics and anti-interference conduction characteristics of the material under different transmission rates and environmental conditions, and combining these with the material's own physical properties to transform them into a dynamic description of material conduction, including: Select the material sample corresponding to the basic material conduction information, conduct signal conduction tests under different transmission rate conditions, record the conduction data of the material sample for signals of different frequencies, and extract the attenuation, transmission delay and signal retention ratio of the signal in the material to form signal attenuation conduction characteristics. The material samples were subjected to anti-interference tests under different environmental conditions, simulating environmental effects such as temperature changes, humidity fluctuations, and electromagnetic interference. The signal transmission stability data of the material samples under different environmental conditions were recorded, and anti-interference transmission characteristics were extracted. Analyze the physical properties of the material itself, including its molecular structure, density, and flexibility, and study the correlation between the material's molecular structure, density, and flexibility and the signal attenuation and conduction characteristics and the anti-interference conduction characteristics, so as to determine the influence of the material's molecular structure, density, and flexibility on its conduction performance. A dynamic model of material conduction performance is established, with changes in transmission rate and environmental conditions as input variables, and the signal attenuation conduction characteristics and anti-interference conduction characteristics as output variables. The dynamic model of material conduction performance is used to reflect the changes in material conduction performance under different combinations of input variables. The dynamic model of material conductivity generates predicted data of material conductivity under different transmission rates and environmental conditions, which supplements the deficiencies of measured data and forms a complete dataset of material conductivity. The material conductivity dataset is standardized to transform different types of conductivity characteristic data into descriptive parameters of a unified dimension, so that different types of conductivity characteristic data are comparable and consistent. The standardized conductivity data, material physical property data, and conductivity variation patterns are integrated to form a dynamic description of the material's conductivity, which includes dynamic conductivity parameters and their changing relationships.

4. The artificial intelligence-based fiber optic patch cord optimization design method according to claim 1, characterized in that, The process of constructing a dynamic virtual transmission environment based on the initial adaptation combination of the path structure and the dynamic scene information, performing real-time signal transmission simulation operations in the dynamic virtual transmission environment, and generating real-time transmission performance feedback includes: Based on the path parameters in the initial adaptation combination of the path structure, and combined with the coordinate dynamic change data and the dynamic relationship of feasible area connectivity in the spatial dynamic digital description, a three-dimensional dynamic virtual space is constructed using virtual simulation technology. The three-dimensional dynamic virtual space is used to respond in real time to the changes in entity coordinates in the fiber optic patch cord application scenario and update the feasible area of ​​the path and the distribution of obstacles. According to the structural composition parameters in the initial adaptation combination of the path structure, a virtual fiber optic patch cord dynamic model is constructed in the three-dimensional dynamic virtual space. The virtual fiber optic patch cord dynamic model is used to restore the dynamic changes of the core structure features, cladding structure features, sheath structure features, and interface connection features in the basic structural composition information, and to reflect the changes in the conduction performance in the dynamic description of material conduction in real time. Based on the dynamic transmission rate index and dynamic signal integrity index in the dynamic requirements description of the scenario, a corresponding dynamic test signal is generated according to the preset signal generation rules. The dynamic test signal can adjust its own parameters as the transmission requirements change, so that the dynamic test signal is consistent with the dynamic signal transmission requirements in the dynamic scenario information. The dynamic test signal is input to the input end of the virtual fiber optic patch cord dynamic model to start the real-time signal transmission simulation process, simulate the transmission process of the signal in the dynamic virtual transmission environment, and record the transmission status data of the signal in different path segments and at different time nodes in real time. In the real-time simulation process of signal transmission, the changes of the dynamic virtual transmission environment are monitored in real time. When the dynamic scene information is updated, the parameters of the virtual fiber optic patch cord dynamic model and the parameters of the dynamic test signal are adjusted synchronously to keep the simulation process synchronized with the changes in the actual fiber optic patch cord application scenario. Key transmission performance data are extracted from the real-time simulation process of signal transmission, including signal strength change data, signal waveform change data, transmission time data, and signal integrity data. The signal strength change data, signal waveform change data, transmission time data, and signal integrity data are used to reflect the dynamic performance of signal transmission in real time. The key transmission performance data are organized according to time series and path segments, and combined with the change records of the dynamic virtual transmission environment and the parameter adjustment records of the virtual fiber optic patch cord dynamic model to form the real-time transmission performance feedback that includes dynamic performance indicators and change trends.

5. The artificial intelligence-based fiber optic patch cord optimization design method according to claim 4, characterized in that, The extraction of key transmission performance data in the real-time simulation process of signal transmission includes: Real-time data acquisition points are set at the input end, output end, and each path segment position of the virtual fiber optic patch cord dynamic model. Each real-time data acquisition point is used to continuously capture instantaneous data during signal transmission. The position of the real-time data acquisition point can be updated synchronously with the adjustment of the deployment path parameters in the initial adaptation combination of the path structure. A data acquisition frequency is set, which is calculated based on a preset sampling rule, the signal transmission rate, and the dynamic change frequency in the dynamic scene information. The instantaneous value of signal strength is captured by the real-time data acquisition points, the signal strength data of each real-time data acquisition point at each time node is recorded, the signal strength change at adjacent time nodes is calculated, and the signal strength change data is formed. The signal strength change data is used to reflect the attenuation of the signal during transmission. Capture signal waveform data from each of the real-time data acquisition points, compare the dynamic test signal waveform at the input end, analyze the amplitude, phase, and frequency changes of the signal waveform, extract waveform distortion features, and form the signal waveform change data. Record the time data of the signal transmission from the input terminal to each of the real-time data acquisition points, calculate the transmission time of the signal in each path segment, analyze the pattern of transmission time change with time and path, and form the transmission time data. The integrity of the signal transmission process is evaluated by signal integrity analysis methods. Jitter data, noise data, and bit error related data of the signal are extracted to form the signal integrity data, which is used to reflect the reliability of signal transmission. The collected signal strength change data, signal waveform change data, transmission time data, and signal integrity data are processed in real time to remove abnormal data, so that the signal strength change data, signal waveform change data, transmission time data, and signal integrity data have accuracy and validity, forming the key transmission performance data used to reflect the dynamic performance of signal transmission in real time.

6. The artificial intelligence-based fiber optic patch cord optimization design method according to claim 4, characterized in that, The step of dynamically adjusting the deployment path parameters in the initial path structure adaptation combination based on the real-time transmission performance feedback through the artificial intelligence dynamic adaptation model to form a multi-round path structure adjustment combination includes: The key transmission performance data in the real-time transmission performance feedback is analyzed, and the signal strength change, transmission time, and signal integrity index are compared with the preset performance threshold. The path segments that do not meet the performance threshold are identified and determined as the target path segments that need to be adjusted in the deployment path in the initial adaptation combination of the path structure. The AI ​​dynamic adaptation model retrieves the dynamic relationships of feasible regional connectivity and the dynamic data of obstacle distribution in the spatial dynamic digital description, analyzes the feasible path options around the target path segment, and finds the path direction that meets the dynamic signal transmission requirements in the dynamic scene information. By combining the dynamic transmission rate indicators and the dynamic description of environmental effects in the dynamic demand description of the scenario, the path length and path direction of the target path segment are adjusted to avoid areas that affect signal transmission performance and optimize the spatial distribution of the target path segment. For the turning positions in the target path segment, analyze the signal transmission data of the turning position in the real-time transmission performance feedback, and combine the flexibility-related characteristics in the material transmission dynamic description to adjust the turning angle and turning radius to reduce signal transmission loss at the turning point. The layout path parameters in the initial adaptation combination of the path structure are updated synchronously to ensure that the adjusted layout path parameters are consistent with the spatial dynamic digital description and remain coherent with other path segments. The adjusted deployment path parameters are matched with the original structural composition parameters in the initial adaptation combination of the path structure to ensure that the structural composition parameters after path adjustment can still meet the signal transmission requirements. If there is a mismatch, the relevant items of the structural composition parameters are initially adjusted. Record the specific content, basis, and expected transmission performance of this path parameter adjustment to form the first round of path structure adjustment combination. Repeat the steps of parsing the key transmission performance data in the real-time transmission performance feedback, finding the path direction that meets the dynamic signal transmission requirements in the dynamic scenario information, adjusting the path length and path direction of the target path segment, updating the deployment path parameters in the initial adaptation combination of the path structure, performing adaptability analysis, and recording the adjustment content to generate the multi-round path structure adjustment combination.

7. The artificial intelligence-based fiber optic patch cord optimization design method according to claim 6, characterized in that, The process involves performing a compatibility analysis between the adjusted deployment path parameters and the original structural composition parameters in the initial adaptation combination of the path structure. This ensures that the structural composition parameters after path adjustment still meet signal transmission requirements. If mismatches exist, the relevant items of the structural composition parameters are initially adjusted, including: The analysis examines the changes in structural requirements due to the adjusted deployment path parameters, including the impact of path length changes on core length requirements, path curvature on structural flexibility requirements, and the environmental requirements of the fiber optic patch cord application scenario on sheath protection requirements. The material conduction dynamic description is retrieved, and based on the transmission distance and environmental conditions after the path adjustment, the material type that matches the transmission distance and environmental conditions after the path adjustment is selected. The material configuration of the core layer and cladding is adjusted so that the signal attenuation conduction characteristics and anti-interference conduction characteristics of the material match the new path conditions. In response to the changes in signal transmission rate and signal integrity requirements after path adjustment, the core diameter parameter in the core structure feature and the cladding thickness parameter in the cladding structure feature of the basic structural composition information are adjusted to optimize the refractive index matching relationship between the core and cladding and improve signal transmission efficiency. Based on the changes in the environment of the fiber optic patch cord application scenario, the type and thickness of the sheath structure features in the basic structural information are adjusted to enhance the sheath's resistance to environmental effects, so that the fiber optic patch cord can maintain stable transmission performance in the new path environment. Analyze the interface connection features in the basic information of the structure composition and their compatibility with the adjusted path, and adjust the interface type and connection method accordingly; The adjusted structural composition parameters are integrated to make the core diameter parameter in the core structure feature, the cladding thickness parameter in the cladding structure feature, the type and thickness of the sheath structure feature, and the type and connection method of the interface mutually compatible, forming structural composition adjustment parameters that match the adjusted layout path parameters; Record the adjustment content, adjustment basis, and adaptation relationship with path parameters of the structural composition parameters, associate the structural composition adjustment parameters with the adjusted layout path parameters, and improve the path structure adjustment combination.

8. The artificial intelligence-based fiber optic patch cord optimization design method according to claim 1, characterized in that, The step of associating and integrating each of the deployment path options with the adapted structural components to form an initial path structure adaptation combination that includes path dynamic parameters, structural dynamic parameters, and the adaptation relationship between the two includes: Extract the path dynamic parameters of each of the deployment path options, including path length, path direction, turning position, and environmental information of the fiber optic patch cord application scenario in which the path is located. The path length, path direction, turning position, and environmental information of the fiber optic patch cord application scenario in which the path is located are directly obtained from the output results of the spatial dynamic digital description and the dynamic association algorithm. Analyze the requirements of the path dynamic parameters of each of the deployment path options on the structural composition, and determine the adaptation range of core layer length, flexibility, protection level and interface type in the structural composition; Structural composition combinations that meet the adaptation range are selected from the dynamic description of the structural composition. Each structural composition combination contains complete parameters of the core structure features, cladding structure features, sheath structure features, and interface connection features in the basic information of the structural composition. The collaborative adaptation algorithm in the AI ​​dynamic adaptation model is used to analyze the degree of adaptation between each of the deployment path options and the selected combination of structural components, evaluate the expected signal transmission performance after the combination of the two, and form an adaptation degree evaluation result. Based on the adaptation evaluation results, and according to the preset adaptation sorting rules, the structural composition combination with the highest adaptation evaluation result is selected for association for each deployment path option; The path dynamic parameters of the layout path options are associated and bound with the structural dynamic parameters of the optimal combination of the structure components, so as to clarify the adaptation relationship and mutual influence between the two. Organize each associated and bound combination according to a unified format, clarify the detailed information of path dynamic parameters, structure dynamic parameters and their adaptation relationship, and form the initial adaptation combination of the path structure.

9. An artificial intelligence-based fiber optic patch cord optimization design system, characterized in that, include: processor; A machine-readable storage medium for storing machine-executable instructions of the processor; The processor is configured to execute the AI-based fiber optic patch cord optimization design method according to any one of claims 1 to 8 by executing the machine-executable instructions.

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