A new line railway transmission and data network port intelligent planning method and system

Abstract

The application provides a new line railway transmission and data network port intelligent planning method and system, relates to the railway communication technical field, and solves the technical problems of low efficiency, easy error and difficulty in meeting complex design specifications caused by the dependence of the existing railway communication port planning on manual experience. The method comprises the following steps: obtaining a business requirement list and a device resource list; constructing a constraint optimization model based on a preset port planning rule library; based on the business requirement list and the device resource list, the constraint optimization model is optimized and solved by a constraint optimization algorithm to obtain a port allocation result; based on the port location in the port allocation result and the physical distance between the device cabinet and the ODF frame, the tail fiber length is calculated; the port allocation result and the tail fiber length are structured and coded and integrated to obtain a port configuration account. The application is used in the new line railway transmission and data network port planning process.

Description

Technical Field

[0001] This application relates to the field of railway communication technology, and in particular to a method and system for intelligent planning of transmission and data network ports on new railway lines. Background Technology

[0002] In the construction of new railway communication systems, transmission networks and data communication networks need to provide standardized data channels for multiple disciplines such as communication, signaling, power, traction power supply, and information. This involves dozens of subsystems, including access networks, dispatch communication, wireless communication, video surveillance, centralized train dispatching, and remote power control. Interface types cover various forms such as E1, FE electrical ports, FE optical ports, and GE optical ports, and must strictly adhere to the mandatory rules in the "Railway Communication Design Specification" regarding paired configuration of E1 interfaces, physical isolation of critical services, and optical interface rate matching. Currently, port planning mainly relies on manual table lookups, experience-based judgment, and manual compilation in Excel. Due to the complex interdisciplinary nature and strong rule dependencies, configuring thousands of ports across more than ten stations on a new line is a large-scale and time-consuming task. Manual ledgers are prone to omissions and misconfigurations, and the risks are high during the later cutover and commissioning phases. Furthermore, existing network management or design software focuses primarily on operational management and lacks intelligent tools for front-end planning during the construction phase, failing to automatically map business requirements to specific equipment port templates. Therefore, there is an urgent need for an intelligent port planning method that integrates multi-disciplinary business requirements, design rules, and equipment models. Summary of the Invention

[0003] This application provides a new method and system for intelligent planning of railway transmission and data network ports, which solves the technical problems of low efficiency, error-proneness and difficulty in meeting complex design specifications caused by the reliance on manual experience in existing railway communication port planning.

[0004] To achieve the above objectives, this application adopts the following technical solution:

[0005] Firstly, a new intelligent planning method for railway transmission and data network ports is provided, including:

[0006] Obtain a list of business requirements and a list of equipment resources;

[0007] A constraint optimization model is constructed based on a pre-defined port planning rule base;

[0008] Based on the business requirements list and equipment resource list, the constraint optimization model is optimized and solved using a constraint optimization algorithm to obtain the port allocation results;

[0009] The length of the pigtail is calculated based on the port location and the physical distance between the equipment cabinet and the ODF rack in the port allocation results.

[0010] The port allocation results and fiber optic pigtail lengths are structured, encoded, and integrated to obtain a port configuration ledger.

[0011] Based on the above technical solutions, this application provides an intelligent planning method for new railway transmission and data network ports. By constructing a constrained optimization model and using algorithms for automatic solution, it achieves intelligent planning throughout the entire process from business requirements to port configuration. Firstly, this completely liberates planning and design personnel from tedious and error-prone manual table lookups and matching, greatly improving planning efficiency and response speed. Secondly, the rule-based model construction ensures that the planning results strictly comply with mandatory industry standards, fundamentally eliminating security and compliance errors caused by human negligence. Simultaneously, automated calculation of fiber optic cable length and structured ledger generation not only reduce material waste and secondary measurement work during on-site construction but also output standardized configuration data that can be directly used for subsequent construction and maintenance, breaking down the barriers between design and implementation. Overall, this method transforms discrete operations relying on personal experience into a rule-driven, traceable, standardized process, significantly improving the reliability, consistency, and scalability of railway communication system design.

[0012] In conjunction with the first aspect above, in one possible implementation, the constrained optimization model is constructed in the following ways:

[0013] Define model parameters and decision variables;

[0014] Based on model parameters and decision variables, the port planning rule base is quantified into mathematical constraint expressions to obtain a set of constraint conditions; wherein, the set of constraint conditions includes hard constraints and soft constraints;

[0015] Construct a multi-objective optimization function based on soft constraints and preset optimization objectives;

[0016] The constraint set and multi-objective optimization function are integrated based on the CSP solution framework to obtain the constrained optimization model.

[0017] In conjunction with the first aspect above, in one possible implementation, the method for obtaining the set of constraints includes:

[0018] The mandatory railway design specifications in the port planning rule base are transformed into hard constraints; wherein, the hard constraints include port type matching constraints, primary and backup service pairing configuration constraints, and port uniqueness constraints.

[0019] The engineering optimization guidance requirements in the port planning rule base are transformed into soft constraints; wherein, the soft constraints include physical isolation constraints for critical services, convergence constraints for service resources, and fragmentation constraints for port allocation;

[0020] By integrating hard and soft constraints, a set of constraints is obtained.

[0021] In conjunction with the first aspect above, in one possible implementation, the step of optimizing the constrained optimization model using a constrained optimization algorithm includes:

[0022] The business requirement list is prioritized based on a preset business level classification standard to obtain a business requirement sequence.

[0023] Based on the equipment resource list, the CSP solver is used to search for candidate ports in the business requirement sequence to obtain preliminary port matching results.

[0024] Conflict detection is performed on the preliminary port matching results based on the set of constraints.

[0025] If a conflict is detected, a conflict detection result is generated and repaired to obtain a conflict-free matching result;

[0026] If no conflict is detected, the initial port matching result is marked as a conflict-free matching result;

[0027] Substitute the conflict-free matching result into the multi-objective optimization function and calculate the function value. Select the allocation scheme corresponding to the minimum function value to obtain the port allocation result.

[0028] In conjunction with the first aspect above, in one possible implementation, the method for obtaining the conflict detection result includes:

[0029] Following the principle of prioritizing hard constraints and then soft constraints, the constraint compliance of each port's initial matching result is verified one by one.

[0030] If a violation of hard constraints is detected, a system alarm is triggered, and the conflict level is marked as severe.

[0031] If a violation of a soft constraint is detected, the conflict level is marked as "normal" and no alarm is triggered.

[0032] Record the service name, port location, conflict type, and conflict level of the conflict, and generate conflict detection results.

[0033] In conjunction with the first aspect above, in one possible implementation, generating and repairing the conflict detection result includes:

[0034] The conflict detection results are classified into hard constraint conflicts and soft constraint conflicts according to the conflict type.

[0035] For hard constraint conflicts, perform a backtracking operation to allocate low-priority services and enable backup slots for repair.

[0036] The repair operations for resolving soft constraint conflicts are not critical business operations and involve adjusting port allocation positions;

[0037] A second constraint check is performed on the repaired matching results. If there are no hard constraint conflicts, a conflict-free matching result is generated; if hard constraint conflicts still exist, insufficient device resources are reported and a prompt to adjust the device configuration is given.

[0038] In conjunction with the first aspect above, in one possible implementation, the method for obtaining the pigtail length includes:

[0039] Extract optical interface port information from the port allocation results, and associate the measured physical wiring distance between the equipment cabinet and ODF rack corresponding to each optical interface port to generate a detailed list of optical interface ports;

[0040] Call the formula for calculating pigtail length The system performs batch automated calculations on the detailed list of optical interface ports to obtain the original pigtail lengths; where D is the measured physical cabling distance between the equipment cabinet and the ODF rack. This is the redundancy coefficient. For the length of the container;

[0041] Based on the pre-established railway communication construction practices, the original pigtail length is standardized to obtain the pigtail length.

[0042] In conjunction with the first aspect above, in one possible implementation, the redundancy coefficient is set in the following ways:

[0043] Identify the core and secondary influencing factors; the core influencing factors include cabling complexity, site service level, and fiber optic cable laying method, while the secondary influencing factors include construction technology level, operation and maintenance requirements, and data center environment.

[0044] The core influencing factors are matched with a preset redundancy coefficient benchmark table to obtain the initial redundancy coefficient.

[0045] The initial redundancy coefficient is adaptively adjusted based on secondary influencing factors to obtain the candidate redundancy coefficient.

[0046] The candidate redundancy coefficients are verified for practical engineering adaptability to obtain the redundancy coefficients.

[0047] In conjunction with the first aspect above, in one possible implementation, the structured encoding and integration of the port allocation results and pigtail lengths includes:

[0048] Based on the preset port system hierarchy, a unique machine-readable code is generated for the port allocation result and the pigtail length allocation, thus generating a set of coded planning information.

[0049] The coded planning information set is integrated with multi-dimensional attributes and the consistency of the information is verified to obtain integrated planning data; wherein, the multi-dimensional attributes include business attributes, equipment attributes and engineering attributes;

[0050] The integrated planning data is populated into the preset configuration ledger template to obtain the port configuration ledger; the port configuration ledger has also undergone multi-format adaptation processing and supports multi-format export.

[0051] Secondly, a new intelligent planning system for railway transmission and data network ports is provided, including a list acquisition module, a model building module, an optimization solution module, a pigtail calculation module, and a ledger generation module;

[0052] The list acquisition module is used to acquire the business requirement list and the equipment resource list;

[0053] The model building module is used to build a constraint optimization model based on a preset port planning rule base.

[0054] The optimization solution module is used to optimize and solve the constraint optimization model based on the business requirement list and the equipment resource list, and obtain the port allocation result by means of the constraint optimization algorithm;

[0055] The pigtail calculation module is used to calculate the pigtail length based on the port location and the physical distance between the equipment cabinet and the ODF rack in the port allocation result.

[0056] The ledger generation module is used to perform structured encoding and integration of port allocation results and pigtail lengths to obtain a port configuration ledger.

[0057] This application provides a method and system for intelligent port planning of transmission and data networks for new railway lines. By constructing a multi-disciplinary business requirement model and a railway port planning rule base, combined with equipment port templates, and employing a constraint optimization algorithm, it automates the entire process from business requirement input to port configuration ledger generation, effectively solving problems such as large workload, error susceptibility, and time-consuming ledger production in port planning during new line construction. The optimization model based on hard and soft constraints ensures that the planning scheme strictly complies with industry design specifications, and mandatory requirements such as physical isolation of key services and primary / backup paired configuration are automatically met. The automatic calculation function of pigtail length reduces on-site cutting waste and improves the accuracy of optical cable construction. The standardized output port configuration ledger can be directly used for construction briefing and subsequent operation and maintenance, achieving seamless integration from design to construction. In addition, the solution supports rapid expansion of new business types, requiring only an update of the rule base without rewriting the algorithm, and has good scalability and engineering practicality.

[0058] It should be understood that the descriptions of technical features, technical solutions, beneficial effects, or similar language in this application do not imply that all features and advantages can be achieved in any single embodiment. Rather, it is understood that the description of a feature or beneficial effect means that a specific technical feature, technical solution, or beneficial effect is included in at least one embodiment. Therefore, the descriptions of technical features, technical solutions, or beneficial effects in this specification do not necessarily refer to the same embodiment. Furthermore, the technical features, technical solutions, and beneficial effects described in this embodiment can be combined in any suitable manner. Those skilled in the art will understand that embodiments can be implemented without one or more specific technical features, technical solutions, or beneficial effects of a particular embodiment. In other embodiments, additional technical features and beneficial effects may be identified in specific embodiments that do not embody all embodiments. Attached Figure Description

[0059] Figure 1 A flowchart illustrating a new railway transmission and data network port intelligent planning method provided in this application embodiment;

[0060] Figure 2 A schematic diagram illustrating the construction process of the constraint optimization model provided in the embodiments of this application;

[0061] Figure 3 A schematic diagram illustrating the process of obtaining conflict detection results provided in an embodiment of this application;

[0062] Figure 4 This is a system architecture diagram of a new railway transmission and data network port intelligent planning system provided in an embodiment of this application. Detailed Implementation

[0063] In the description of this application, unless otherwise stated, " / " means "or," for example, A / B can mean A or B. The "and / or" in this document is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, and B alone. Furthermore, "at least one" means one or more, and "multiple" means two or more. The terms "first," "second," etc., do not limit the quantity or order of execution, and "first," "second," etc., do not necessarily imply differences.

[0064] It should be noted that, in this application, the terms "exemplary" or "for example" are used to indicate that something is being described as an example, illustration, or illustration. Any embodiment or design described as "exemplary" or "for example" in this application should not be construed as being more preferred or advantageous than other embodiments or design solutions. Specifically, the use of terms such as "exemplary" or "for example" is intended to present the relevant concepts in a concrete manner.

[0065] To address the technical problems of inefficiency, error-proneness, and difficulty in meeting complex design specifications caused by the reliance on manual experience in existing railway communication port planning, this application provides an intelligent planning method for transmission and data network ports on new railway lines. The method includes:

[0066] Obtain a list of business requirements and a list of equipment resources;

[0067] A constraint optimization model is constructed based on a pre-defined port planning rule base;

[0068] Based on the business requirements list and equipment resource list, the constraint optimization model is optimized and solved using a constraint optimization algorithm to obtain the port allocation results;

[0069] The length of the pigtail is calculated based on the port location and the physical distance between the equipment cabinet and the ODF rack in the port allocation results.

[0070] The port allocation results and fiber optic pigtail lengths are structured, encoded, and integrated to obtain a port configuration ledger.

[0071] Based on this, the technical problems of low efficiency, error-proneness, and difficulty in meeting complex design specifications caused by the reliance on manual experience in the planning of existing railway communication ports have been solved.

[0072] like Figure 1 As shown in the embodiment of this application, a new railway transmission and data network port intelligent planning method is provided, including:

[0073] S201. Obtain the business requirements list and equipment resource list.

[0074] For example, in the second phase of the XX Railway project, basic information of all eight stations along the line was imported from the BIM platform via an interface, including the station's equipment room number, equipment cabinet and ODF rack coordinates. The business requirements of each specialty were automatically parsed from the preliminary design communication book, as shown in Table 1. A structured business requirement list was generated, including 2 FE optical ports for signal CTC, 2 FE optical ports for signal section logic check, 2 FE optical ports for power remote control SCADA, 2 GE optical ports for communication GSM-R, 2 FE electrical ports for communication video surveillance, and 2 E1 interfaces for communication FAS. At the same time, based on the Huawei E9624X equipment model selected for XX station, its physical model was loaded from the built-in equipment port template library to generate an equipment resource list including slots 1 to 8 for general business slots, support for mixed insertion of TND1EX16 and HUNQ2 single boards, and slots 17 and 18 for main control cross boards. The measured distance of 7.5 meters from the equipment cabinet to the ODF rack was obtained as a physical environment parameter, providing a complete data foundation for subsequent planning.

[0075] Table 1

[0076]

[0077] In this embodiment, standardized and structured business requirement lists and equipment resource lists are quickly generated through automatic collection and parsing of multi-source data, avoiding omissions from manual input and ensuring the accuracy and completeness of input data; the automatic association of physical environment parameters provides a reliable basis for the subsequent accurate calculation of pigtail length, significantly improving the efficiency and quality of data preparation.

[0078] S202. Construct a constraint optimization model based on a preset port planning rule base.

[0079] In some implementations, the constrained optimization model is constructed in the following ways: Figure 2 As shown, it includes:

[0080] S21. Define model parameters and decision variables;

[0081] S22. Based on model parameters and decision variables, the port planning rule base is quantified into mathematical constraint expressions to obtain a set of constraint conditions; wherein, the set of constraint conditions includes hard constraints and soft constraints;

[0082] S23. Construct a multi-objective optimization function based on soft constraints and preset optimization objectives;

[0083] S24. Based on the CSP solution framework, the constraint set and multi-objective optimization function are integrated to obtain the constrained optimization model.

[0084] For example, firstly, model parameters and decision variables are defined. For station XX, the service requirement set is defined to include 6 requirements: signal CTC requires 2 FE optical ports, interval logic check requires 2 FE optical ports, power SCADA requires 2 FE optical ports, communication GSM-R requires 2 GE optical ports, video surveillance requires 2 FE electrical ports, and FAS requires 2 E1 interfaces. Each requirement carries attributes such as interface type, quantity, priority, and security isolation identifier. The equipment resource set is generated based on the Huawei E9624X equipment template, including the port information of each board in slots 1 to 8 of the general service slots and the unavailable identifier of the main control board in slots 17 and 18. A binary decision variable x_ij is defined to indicate whether requirement i is allocated to port j. Then, based on the model parameters and decision variables, the port planning rule base is quantified into mathematical constraint expressions, resulting in a set of constraints. Hard constraints include port type matching constraints (each requirement must be assigned a consistent port type), primary / backup service pairing constraints (the two E1 ports of the FAS must be located on different boards), and port uniqueness constraints (each port can only be assigned to one requirement). Soft constraints include critical service physical isolation constraints (CTC and SCADA are penalized if assigned to the same board), and service resource aggregation constraints (non-critical services of the same type should be concentrated to reduce fragmentation). Next, a multi-objective optimization function is constructed based on the soft constraints and preset optimization objectives, comprehensively considering isolation violation penalties, resource fragmentation levels, and fiber optic costs, and assigning weight coefficients w1, w2, and w3 respectively. Finally, the constraint set and multi-objective optimization function are integrated based on the CSP solution framework to obtain a constraint optimization model containing all constraints and objective functions, which is used for subsequent intelligent solving.

[0085] In this embodiment, by precisely quantifying business requirement attributes, equipment physical resources, and industry design specifications into parameters, decision variables, constraints, and objective functions in a mathematical model, a computable expression of complex rules is achieved. This enables the computer to automatically process constraints involving multiple disciplines, ensuring that the model strictly conforms to mandatory specifications while also balancing multiple optimization objectives. This lays a solid mathematical foundation for efficiently solving the optimal port allocation scheme in the future.

[0086] In some implementations, the method for obtaining the set of constraints includes:

[0087] The mandatory railway design specifications in the port planning rule base are transformed into hard constraints; wherein, the hard constraints include port type matching constraints, primary and backup service pairing configuration constraints, and port uniqueness constraints.

[0088] The engineering optimization guidance requirements in the port planning rule base are transformed into soft constraints; wherein, the soft constraints include physical isolation constraints for critical services, convergence constraints for service resources, and fragmentation constraints for port allocation;

[0089] By integrating hard and soft constraints, a set of constraints is obtained.

[0090] For example, the mandatory railway design specifications in the port planning rule base are transformed into hard constraints. For the two FE optical ports of the CTC and the two E1 interfaces of the FAS at station XX, port type matching constraints are established to ensure that each requirement can only be assigned to a port of the same type. For FAS services, a primary and backup service pairing configuration constraint is established, requiring that its two E1 ports must be located on different boards, such as the TGS boards in slots 1 and 12. At the same time, a port uniqueness constraint is established for all services to ensure that each slot and port number combination in the Huawei E9624X equipment can only be assigned to one requirement. The engineering optimization guidance requirements in the port planning rule base are transformed into soft constraints, including physical isolation constraints for critical services, requiring high-security services such as CTC and power SCADA to be assigned to different boards as much as possible to reduce the risk of common failures; service resource aggregation constraints, requiring non-critical services such as video surveillance to be concentrated on the same electrical port board in slots 2 and 11 to save board resources; and port allocation fragmentation constraints to encourage continuous port allocation for future expansion. Finally, all the above hard and soft constraints are integrated to form a complete set of constraints to guide the subsequent optimization solution process.

[0091] In this embodiment of the application, by precisely quantifying the mandatory railway design specifications into hard constraints, the port allocation scheme is ensured to strictly comply with industry regulations, avoiding rule omissions that may occur in manual planning. At the same time, the engineering optimization requirements are quantified into soft constraints, taking into account safety, resource utilization and scalability while meeting the mandatory specifications. This provides a calculable evaluation benchmark for multi-objective optimization and significantly improves the engineering rationality of the planning scheme.

[0092] S203. Based on the business requirements list and equipment resource list, the constraint optimization model is optimized and solved using a constraint optimization algorithm to obtain the port allocation result.

[0093] In some implementations, the step of optimizing the constrained optimization model using a constrained optimization algorithm includes:

[0094] The business requirement list is prioritized based on a preset business level classification standard to obtain a business requirement sequence.

[0095] Based on the equipment resource list, the CSP solver is used to search for candidate ports in the business requirement sequence to obtain preliminary port matching results.

[0096] Conflict detection is performed on the preliminary port matching results based on the set of constraints.

[0097] If a conflict is detected, a conflict detection result is generated and repaired to obtain a conflict-free matching result;

[0098] If no conflict is detected, the initial port matching result is marked as a conflict-free matching result;

[0099] Substitute the conflict-free matching result into the multi-objective optimization function and calculate the function value. Select the allocation scheme corresponding to the minimum function value to obtain the port allocation result.

[0100] For example, firstly, the service requirement list of XX station is prioritized based on a preset service level classification standard. The two FE optical ports for signal CTC are set to priority 1, the two FE optical ports for signal interval logic check are set to priority 2, the two FE optical ports for power remote SCADA are set to priority 3, the two GE optical ports for communication GSM-R are set to priority 4, the two FE electrical ports for communication video surveillance are set to priority 5, and the two E1 interfaces for communication FAS are set to priority 6, thus obtaining a service requirement sequence. Then, based on the resource list of the Huawei E9624X equipment, the CSP solver is used to search for candidate ports in the service requirement sequence. First, available FE optical ports are searched for the CTC requirement (priority 1). Idle port 1 is found as a candidate on the HUNQ2 boards in slots 3 and 10, respectively, obtaining preliminary port matching results. Next, conflict detection is performed on the preliminary port matching results based on the constraint set, which is then used for subsequent interval logic checks. When allocating power SCADA, it was detected that if all allocations were concentrated on the same board in slots 3 and 10, it would violate the soft constraint of physical isolation of critical services. Therefore, a conflict detection result was triggered and repaired. The system adjusted the allocation by backtracking, allocating the interval logic check to port 2 of slots 3 and 10, and power SCADA to port 3, to achieve board-level isolation and obtain a conflict-free matching result. The conflict-free matching result was substituted into a multi-objective optimization function to calculate the function value. After iterative comparison, the allocation scheme corresponding to the minimum function value was selected. Finally, the complete port allocation result was obtained, including FAS allocated to port 1 of the TGS board in slots 1 and 12, CTC allocated to port 1 of slots 3 and 10, interval logic check allocated to port 2 of slots 3 and 10, power SCADA allocated to port 3 of slots 3 and 10, GSM-R allocated to port 1 of slots 4 and 9, and video surveillance allocated to port 1 of slots 2 and 11.

[0101] In this embodiment, priority ranking ensures that critical services have priority access to high-quality port resources. Candidate port search based on CSP solver effectively improves allocation efficiency. Conflict detection and repair mechanism dynamically adjusts the allocation scheme to simultaneously satisfy hard constraints and optimize soft constraints. The resulting multi-objective optimized allocation scheme achieves rational resource utilization while ensuring secure isolation, significantly improving the intelligence level and scheme quality of port planning.

[0102] In some implementations, the method for obtaining the conflict detection results is as follows: Figure 3 As shown, it includes:

[0103] S51. Perform constraint compliance verification on each port preliminary matching result in the order of hard constraints first and soft constraints second.

[0104] S52. If a violation of hard constraints is detected, a system alarm is triggered, and the conflict level is marked as severe;

[0105] S53. If a violation of a soft constraint is detected, the conflict level is marked as "normal" and no alarm is triggered.

[0106] S54. Record the service name, port location, conflict type, and conflict level of the conflict, and generate conflict detection results.

[0107] For example, following the order of hard constraints first and soft constraints second, the initial port matching results of station XX are checked for constraint compliance one by one. When detecting the two E1 interfaces of signal FAS, it is found that the initial allocation scheme assigns both ports to the same TGS board in slot 1, violating the hard constraint of pairing primary and backup services. The system immediately triggers a red alarm and marks the conflict level as severe, the conflict type as pairing configuration conflict, the service name as FAS, and the port positions as port 1 and port 2 of slot 1 in slot 1 in the conflict detection table. When detecting the allocation of signals CTC and power SCADA, it is found that they are assigned to adjacent ports on the same HUNQ2 board in slot 3, violating the soft constraint of physical isolation of critical services. The system marks the conflict level as general, the conflict type as isolation conflict, the service names as CTC and power SCADA, and the port positions as port 1 and port 3 of slot 3 in slot 3 in slot 3 in the conflict detection table, but no system alarm is triggered. The system records the service name, port position, conflict type, and conflict level of all conflicts, generates a complete conflict detection result containing the above two conflict records, and submits it to the subsequent repair module for processing.

[0108] In this embodiment, a hard constraint priority verification mechanism ensures that mandatory rules such as paired configurations are strictly enforced, and an alarm is triggered immediately upon violation, effectively preventing major design flaws. Soft constraint conflicts are graded and labeled without alarms, which both indicates optimization space and avoids excessive interference, allowing engineers to focus on key issues. Detailed conflict records provide complete data support for subsequent accurate repairs, significantly improving the efficiency and accuracy of conflict handling.

[0109] In some implementations, generating and repairing conflict detection results includes:

[0110] The conflict detection results are classified into hard constraint conflicts and soft constraint conflicts according to the conflict type.

[0111] For hard constraint conflicts, perform a backtracking operation to allocate low-priority services and enable backup slots for repair.

[0112] The repair operations for resolving soft constraint conflicts are not critical business operations and involve adjusting port allocation positions;

[0113] A second constraint check is performed on the repaired matching results. If there are no hard constraint conflicts, a conflict-free matching result is generated; if hard constraint conflicts still exist, insufficient device resources are reported and a prompt to adjust the device configuration is given.

[0114] For example, the generated conflict detection results are categorized into hard constraint conflicts and soft constraint conflicts based on conflict type. For the hard constraint conflict where both E1 ports of the FAS service at station XX are allocated to the same TGS board in slot 1, a low-priority service allocation rollback operation is performed. The allocated low-priority video surveillance service is temporarily removed from the TGS board in slot 12. After releasing the board's resources, the second E1 port of the FAS service is reassigned to port 1 of the TGS board in slot 12. Simultaneously, the spare slot resources are used to adjust the video surveillance service to the spare electrical port board in slot 8. For signal CTC and power SCADA conflicts... For soft constraint conflicts caused by the same HUNQ2 board in slot 3, the port allocation position is adjusted. The power SCADA is moved from port 3 of slot 3 to port 3 of the HUNQ2 board in slot 10 to achieve board-level physical isolation from CTC. The system performs a second constraint verification on the repaired matching results to confirm that all hard constraints are met and the soft constraint optimization objectives are improved. A conflict-free matching result containing the above adjustment scheme is generated. If hard constraint conflicts still exist under insufficient resources, the system will report that the current Huawei E9624X device has insufficient E1 port resources and suggest adding TGS board configuration.

[0115] In this embodiment, a layered repair strategy for hard and soft constraint conflicts is adopted to perform mandatory backtracking adjustments for serious issues such as paired configuration violations, ensuring absolute compliance of the solution; port positions are fine-tuned for optimization issues such as insufficient isolation, improving the quality of the solution while meeting hard constraints; a secondary verification mechanism ensures the reliability of the repair results, and the resource shortage feedback function provides a clear direction for device configuration optimization, significantly improving the processing efficiency and solution availability in complex conflict scenarios.

[0116] Through the collaborative mechanism of priority sorting, hierarchical conflict detection and graded repair in S203 above, a closed-loop optimization solution architecture for the special characteristics of railway communication port planning is constructed. Each step is interdependent and progressive, and together they solve the technical problem of efficiently generating high-quality planning schemes under complex constraints.

[0117] First, priority ranking ensures that critical business operations receive resources first, laying a reasonable processing order for subsequent conflict detection and repair. The candidate port search of the CSP solver generates preliminary matching results, while the subsequent conflict detection and repair mechanism is specifically designed for the multi-rule intertwining problem that is common in railway planning. The two work together to avoid a large number of illegal allocations that may be caused by simple search.

[0118] Secondly, the hard constraint priority verification and hierarchical labeling mechanism work in close coordination with conflict detection: hard constraint priority ensures that mandatory specifications such as E1 pair configuration and port uniqueness are strictly monitored. Once a violation is detected, an alarm is immediately triggered and a severe level is marked. This directly addresses the pain point of easy omission and misconfiguration in manual planning. Soft constraint conflicts are marked as general level but no alarm is triggered. This not only indicates the optimization space but also avoids excessive interference, allowing engineers to focus on key issues. This hierarchical strategy significantly improves the pertinence of conflict handling.

[0119] Finally, the hard constraint conflict backtracking and repair, and the soft constraint conflict optimization and adjustment, form a precise mapping with the conflict detection results: for hard constraint conflicts, mandatory repairs such as backtracking low-priority services and enabling spare slots are performed; for soft constraint conflicts, optimization adjustments are made by merging non-critical services and adjusting port positions. This layered repair strategy ensures both the absolute satisfaction of mandatory rules and the continuous improvement of optimization goals. The secondary constraint verification mechanism and conflict detection form a closed-loop feedback to ensure that the repaired solution has no hard constraint conflicts. If resources are insufficient, adjustment suggestions are provided to give engineers clear decision support.

[0120] The aforementioned technical solution forms a complete intelligent solution technology chain through prioritizing resource allocation, prioritizing hard constraints to ensure compliance with standards, using hierarchical conflict detection to accurately locate problems, employing layered repair strategies to address conflicts specifically, and conducting secondary verification to close the loop and validate the results. This collaborative architecture is not a conventional application of general optimization methods, but rather a design specifically tailored to the characteristics of railway communication port planning, which involves the interweaving of multiple professional rules and the coexistence of hard standards and optimization objectives. It inevitably achieves the automatic generation of high-quality planning schemes with good isolation, high resource utilization, and strong constructability while meeting all mandatory standards, fundamentally solving the technical problems of low efficiency, error-proneness, and difficulty in balancing multiple constraints in traditional manual planning.

[0121] S204. Based on the port location and physical distance between the equipment cabinet and the ODF rack in the port allocation results, the length of the pigtail is calculated.

[0122] In some implementations, the method for obtaining the pigtail length includes:

[0123] Extract optical interface port information from the port allocation results, and associate the measured physical wiring distance between the equipment cabinet and ODF rack corresponding to each optical interface port to generate a detailed list of optical interface ports;

[0124] Call the formula for calculating pigtail length The system performs batch automated calculations on the detailed list of optical interface ports to obtain the original pigtail lengths; where D is the measured physical cabling distance between the equipment cabinet and the ODF rack. This is the redundancy coefficient. For the length of the container;

[0125] Based on the pre-established railway communication construction practices, the original pigtail length is standardized to obtain the pigtail length.

[0126] For example, extract all optical interface port information from the port allocation results of station XX, including the allocation of 2 FE optical ports for signal CTC to slot 3 port 1 and slot 10 port 1, the allocation of 2 FE optical ports for interval logic check to slot 3 port 2 and slot 10 port 2, the allocation of 2 FE optical ports for power SCADA to slot 3 port 3 and slot 10 port 3, and the allocation of 2 GE optical ports for communication GSM-R to slot 4 port 1 and slot 9 port 1. Then, associate this with the measured physical cabling distance of 7.5 meters between the corresponding equipment cabinet and ODF rack for each optical interface port, and generate a packet. The system generates a detailed list of the eight optical interface ports mentioned above. It then calls the formula for calculating the pigtail length: L equals D multiplied by the redundancy coefficient plus the reserved length, where D is 7.5 meters, the redundancy coefficient is 1.2, and the reserved length is 2.0 meters. This formula is used to perform batch automated calculations on the detailed list of optical interface ports, resulting in an original pigtail length of 11.0 meters. Based on the pre-defined railway communication construction practices requiring pigtail lengths to be accurate to one decimal place, the system performs precision standardization processing on the original pigtail length, ultimately obtaining a uniform pigtail length of 11.0 meters for all eight optical interface ports, and writes this information into the port configuration ledger.

[0127] In this embodiment, the length of the pigtail for each optical interface is accurately obtained through batch automated calculation, avoiding material waste and on-site cutting problems caused by manual estimation. Standardized processing ensures that the pigtail length meets the construction specifications, providing a precise basis for subsequent optical cable procurement and on-site splicing, and effectively improving the efficiency and accuracy of optical cable construction.

[0128] In some implementations, the redundancy coefficient is set in the following ways:

[0129] Identify the core and secondary influencing factors; the core influencing factors include cabling complexity, site service level, and fiber optic cable laying method, while the secondary influencing factors include construction technology level, operation and maintenance requirements, and data center environment.

[0130] The core influencing factors are matched with a preset redundancy coefficient benchmark table to obtain the initial redundancy coefficient.

[0131] The initial redundancy coefficient is adaptively adjusted based on secondary influencing factors to obtain the candidate redundancy coefficient.

[0132] The candidate redundancy coefficients are verified for practical engineering adaptability to obtain the redundancy coefficients.

[0133] For example, the system first obtains the core and secondary influencing factors. For XX Station of the XX Railway Phase II project, the core influencing factors include: the routing complexity is classified as medium based on the presence of two 90-degree turns as determined by on-site survey; the station's business level is classified as high due to the inclusion of high-security services such as CTC and SCADA; and the fiber optic cable laying method is cable tray laying within the equipment room. Among the secondary influencing factors, the construction process level adopts Level 1 construction standards; the maintenance requirements are classified as high due to frequent future expansions; and the equipment room environment is characterized by good temperature and humidity control. The system then matches the core influencing factors with a preset redundancy coefficient benchmark table, with medium routing complexity corresponding to a benchmark coefficient of 1. 2. For sites with high service levels, the redundancy coefficient is increased by 0.05 based on the baseline, while the cable tray laying method remains unchanged, resulting in an initial redundancy coefficient of 1.25. Based on secondary influencing factors, the initial redundancy coefficient is adaptively adjusted: the first-level construction process level is reduced by 0.02, the high maintenance requirement level is increased by 0.03, and the good data center environment remains unchanged, resulting in a candidate redundancy coefficient of 1.26. Next, the candidate redundancy coefficient is verified for its practical engineering adaptability. Three simulations of fiber optic cable laying are performed at the XX site. The measured length and the theoretical calculation error are both within the allowable range. Finally, the redundancy coefficient of 1.26 is determined for the calculation of fiber optic cable length at this site.

[0134] In this embodiment, the redundancy coefficient is accurately set through comprehensive evaluation and adaptive adjustment of multi-dimensional influencing factors. It takes into account core factors such as wiring complexity and business importance, as well as actual conditions such as construction technology and operation and maintenance needs. The engineering verification mechanism ensures the reliability of the coefficient, effectively avoiding material waste or insufficient length caused by improper redundancy coefficient, and improving the accuracy of pigtail length calculation and engineering adaptability.

[0135] S205. The port allocation results and fiber optic pigtail lengths are structured, encoded, and integrated to obtain the port configuration ledger.

[0136] In some implementations, the structured encoding and integration of port allocation results and pigtail lengths includes:

[0137] Based on the preset port system hierarchy, a unique machine-readable code is generated for the port allocation result and the pigtail length allocation, thus generating a set of coded planning information.

[0138] The coded planning information set is integrated with multi-dimensional attributes and the consistency of the information is verified to obtain integrated planning data; wherein, the multi-dimensional attributes include business attributes, equipment attributes and engineering attributes;

[0139] The integrated planning data is populated into the preset configuration ledger template to obtain the port configuration ledger; the port configuration ledger has also undergone multi-format adaptation processing and supports multi-format export.

[0140] For example, based on the port allocation results of station XX at the preset port system level and the unique machine-readable code for the fiber optic pigtail length allocation, the signal CTC is assigned to the FE optical port of slot 3 port 1 and coded as XH-E96-03-01-FE, the communication GSM-R is assigned to the GE optical port of slot 4 port 1 and coded as XH-E96-04-01-GE, and the FAS is assigned to the E1 interface of slot 1 port 1 and coded as XH-E96-01-01-E1. The fiber optic pigtail length of 11.0 meters corresponding to all optical ports is associated with each code, generating a coded planning information set containing the above codes and corresponding fiber optic pigtail lengths. The system integrates the coded planning information set with multi-dimensional attributes, including service attributes such as CTC belonging to signal specialties, GSM-R belonging to communication specialties, and FAS belonging to communication specialties, and equipment attributes such as slot 3 port... Port 1 in slot 4 is located on the HUNQ2 board, port 1 in slot 4 is located on the optical port board, and port 1 in slot 1 is located on the TGS board. Engineering attributes such as fiber optic pigtail length of 11.0 meters are associated, integrated, and the information consistency is verified to ensure that the business, equipment, and engineering data corresponding to each port code are completely matched to obtain integrated planning data. The system fills the integrated planning data into the preset configuration ledger template and generates a port configuration ledger containing complete fields such as station XX, professional signal, subsystem CTC, interface type FE optical port, quantity 2, equipment model Huawei E9624X, slot 3 and slot 10, single board type HUNQ2, port number 1 and 1, and fiber optic pigtail length of 11.0 meters. At the same time, multi-format adaptation processing is performed, supporting one-click export of multiple formats such as Excel for construction briefing, PDF for design archiving, and JSON for BIM system integration.

[0141] In this embodiment, each port is accurately identified and tracked throughout its entire lifecycle through a unique machine-readable code. The integration of multi-dimensional attributes ensures the unified association of business needs, equipment configurations, and engineering parameters. The standardized ledger templates and multi-format export functions meet the diverse usage needs of different stages of design, construction, and operation and maintenance, significantly improving the usability of planning results and the efficiency of data flow.

[0142] Based on the above technical solutions, this application provides an intelligent port planning method for new railway transmission and data networks. By constructing a multi-disciplinary business requirement model and a railway port planning rule base, combined with equipment port templates, and employing a constraint optimization algorithm, the entire process from business requirement input to port configuration ledger generation is automated. This effectively solves the problems of large workload, error susceptibility, and time-consuming ledger creation in port planning during new line construction. The optimization model based on hard and soft constraints ensures that the planning scheme strictly complies with industry design specifications, and mandatory requirements such as physical isolation of key businesses and primary / backup paired configuration are automatically met. The automatic calculation function for fiber optic pigtail length reduces on-site cutting waste and improves the accuracy of optical cable construction. The standardized output port configuration ledger can be directly used for construction briefings and subsequent operation and maintenance, achieving seamless integration from design to construction. In addition, the solution supports rapid expansion of new business types, requiring only an update to the rule base without rewriting the algorithm, demonstrating good scalability and engineering practicality.

[0143] In one possible implementation, this application embodiment also provides a new railway transmission and data network port intelligent planning system 100, such as... Figure 4 As shown, the system 100 includes a list acquisition module 10, a model building module 20, an optimization solution module 30, a pigtail calculation module 40, and a ledger generation module 50;

[0144] The list acquisition module 10 is used to acquire the business requirement list and the equipment resource list;

[0145] The model building module 20 is used to build a constraint optimization model based on a preset port planning rule base;

[0146] The optimization solution module 30 is used to optimize and solve the constraint optimization model based on the business requirement list and the equipment resource list, and obtain the port allocation result by means of the constraint optimization algorithm;

[0147] The pigtail calculation module 40 is used to calculate the pigtail length based on the port location and the physical distance between the equipment cabinet and the ODF rack in the port allocation result.

[0148] The ledger generation module 50 is used to perform structured encoding and integration of port allocation results and pigtail lengths to obtain a port configuration ledger.

[0149] For example, in the application of XX Railway Phase II XX Station, the list acquisition module 10 imports the basic information of all 8 stations along the line from the BIM platform via an interface. It automatically parses the service requirements in the preliminary design communication booklet, such as the need for 2 FE optical ports for signal CTC, 2 FE optical ports for signal section logic check, 2 FE optical ports for power remote control SCADA, 2 GE optical ports for communication GSM-R, 2 FE electrical ports for communication video surveillance, and 2 E1 interfaces for communication FAS. It generates a structured list of service requirements and loads the physical model from the device port template library according to the Huawei E9624X device model. It generates a list of equipment resources including general service slots 1 to 8 and main control cross-connect boards in slots 17 and 18, and obtains the associated equipment requirements. The measured distance from the backup cabinet to the ODF rack is 7.5 meters. Model building module 20, based on the business requirement list, equipment resource list, and a pre-defined port planning rule base, defines the decision variable x_ij, transforming mandatory specifications such as E1 paired configuration into hard constraints and optimization requirements such as physical isolation of critical services into soft constraints. It constructs a multi-objective optimization function containing hard constraints such as port type matching, primary / backup paired configuration, and port uniqueness, as well as soft constraints such as isolation penalties and resource fragmentation. This is integrated to form a constraint optimization model based on the CSP solution framework. Optimization solution module 30 follows the order of CTC priority 1, interval logic check priority 2, SCADA priority 3, GSM-R priority 4, video surveillance priority 5, and FAS priority 6. The process begins by searching for candidate ports using the CSP solver. A hard constraint conflict is triggered when both E1 ports of the FAS are assigned to the same TGS board in slot 1. Backtracking is then performed, moving the video surveillance to the spare board in slot 8, reassigning the second FAS port to the TGS board in slot 12, and assigning CTC to port 1 of the HUNQ2 board in slots 3 and 10, interval logic check to port 2 of slots 3 and 10, SCADA to port 3 of slots 3 and 10, GSM-R to port 1 of slots 4 and 9, and video surveillance to port 1 of slots 2 and 11. After obtaining conflict-free matching results, these are input into a multi-objective optimization function, and the final port assignment corresponding to the minimum function value is selected. As a result, the pigtail calculation module 40 extracts the information of 8 optical interface ports from the allocation results, associates it with the measured distance of 7.5 meters, calls the pigtail length calculation formula L equals 7.5 multiplied by the redundancy coefficient 1.2 plus the coiled length of 2.0 meters, and calculates the original pigtail length of 11.0 meters in batches. It then performs precision standardization processing according to construction practices, and finally determines that the length of all optical port pigtails is 11.0 meters. The ledger generation module 50 assigns a unique machine-readable code to each port, such as XH-E96-03-01-FE, integrates business attributes, equipment attributes and engineering attributes in multiple dimensions, fills it into the preset ledger template, and generates a ledger containing XX station, signal CTC, FE optical port, slot 3 and slot 10, port number 1 and 1, and pigtail length 11.The system provides a complete port configuration ledger with fields such as 0 meters, and supports one-click export to multiple formats including Excel, PDF, and JSON.

[0150] Based on the above technical solution, the list acquisition module realizes automatic collection and structuring of multi-source data, the model building module accurately quantifies industry standards into computable constraints, the optimization solution module generates high-quality allocation schemes based on priority and conflict repair, the pigtail calculation module automatically completes accurate length calculation, and the ledger generation module outputs standardized multi-format results. The collaborative work of these modules realizes full-process automation from requirement input to configuration ledger, significantly improving planning efficiency and accuracy, ensuring that the scheme strictly complies with design specifications, and providing a reliable data foundation for construction and operation and maintenance.

[0151] The technical solution of this application is not a simple application of general optimization methods, but rather addresses the unique challenges of "port planning for new railway communication systems" in a specific scenario. It constructs a closed-loop collaborative architecture covering the entire process, from data acquisition to final ledger generation. Each step is closely linked and works synergistically, inevitably solving the technical problems of inefficiency, error-proneness, and difficulty in meeting complex design specifications caused by reliance on manual experience in existing technologies. Specifically:

[0152] First, the inventory acquisition module not only collects business requirements from multiple disciplines, but also simultaneously introduces device port templates and physical environment distances, unifying and structuring multi-source heterogeneous data such as design documents, equipment models, and data center layouts, laying a precise domain data foundation for subsequent planning.

[0153] Secondly, the model building module quantifies the mandatory railway design specifications (such as E1 pair configuration and port type matching) into hard constraints, transforms the engineering optimization orientation (such as physical isolation of key businesses and minimization of resource fragmentation) into soft constraints, and constructs a multi-objective optimization function, so that the optimization model is fully embedded with the industry knowledge of railway communication, rather than a general mathematical programming model.

[0154] Furthermore, the optimization solution module prioritizes solutions based on preset business levels. In addition to the CSP solver search, a conflict detection and hierarchical repair mechanism based on hard constraints is specially designed: serious conflicts that violate hard constraints trigger backtracking and the activation of spare slots, while soft constraint conflicts are optimized by adjusting port positions or combining non-critical business functions. Secondary verification ensures that the final solution strictly complies with mandatory specifications, which directly addresses the pain point of easy omissions and misconfigurations in manual planning.

[0155] Next, the pigtail calculation module introduces an adaptive redundancy coefficient setting (considering factors such as wiring complexity and station service level) for the actual wiring distance in the railway equipment room, and calculates the pigtail length accurately in batches, solving the problem of waste during on-site cutting.

[0156] Finally, the ledger generation module outputs standardized multi-format ledgers through unique machine-readable codes and multi-dimensional attribute integration, achieving seamless integration from design results to construction and operation.

[0157] The aforementioned modules do not exist in isolation, but rather form a complete closed loop from "demand input → constraint modeling → intelligent solution → engineering calculation → output results". The customized design of each step serves the specific characteristics of railway port planning. Through synergy, it has for the first time achieved integrated planning that automatically integrates industry standards, equipment physical limitations and engineering parameters, thereby inevitably improving efficiency, eliminating human error and ensuring compliance with standards. This systematic solution for specific technical problems has outstanding substantive features and significant progress.

[0158] In implementation, each step of the method provided in this embodiment can be completed by integrated logic circuits in the processor or by instructions in software form. The steps of the method disclosed in the embodiments of this application can be directly manifested as being executed by a hardware processor, or being executed by a combination of hardware and software modules in the processor.

[0159] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented using software programs, implementation can be, in whole or in part, in the form of a computer program product. This computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium accessible to a computer or a data storage device containing one or more servers, data centers, etc., that can be integrated with the medium. The available media can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., DVDs), or semiconductor media (e.g., solid-state disks (SSDs)).

[0160] Although this application has been described herein in conjunction with various embodiments, those skilled in the art, by reviewing the accompanying drawings, disclosure, and appended claims, will understand and implement other variations of the disclosed embodiments in carrying out the claimed application. In the claims, the word "comprising" does not exclude other components or steps, and "a" or "an" does not exclude multiple instances. A single processor or other unit can implement several functions listed in the claims. While different dependent claims may recite certain measures, this does not mean that these measures cannot be combined to produce good results.

[0161] Although this application has been described in conjunction with specific features and embodiments, it is obvious that various modifications and combinations can be made thereto without departing from the spirit and scope of this application. Accordingly, this specification and drawings are merely exemplary illustrations of this application as defined by the appended claims, and are considered to cover any and all modifications, variations, combinations, or equivalents within the scope of this application. Clearly, those skilled in the art can make various alterations and modifications to this application without departing from the spirit and scope of this application. Thus, if such modifications and modifications of this application fall within the scope of the claims of this application and their equivalents, this application is also intended to include such modifications and modifications.

Claims

1. A method for intelligent planning of transmission and data network ports on new railway lines, characterized in that, include: Obtain a list of business requirements and a list of equipment resources; A constraint optimization model is constructed based on a pre-defined port planning rule base; Based on the business requirements list and equipment resource list, the constraint optimization model is optimized and solved using a constraint optimization algorithm to obtain the port allocation results; The length of the pigtail is calculated based on the port location and the physical distance between the equipment cabinet and the ODF rack in the port allocation results. The port allocation results and pigtail lengths are structured and integrated to obtain a port configuration ledger; The method for constructing the constrained optimization model includes: Define model parameters and decision variables; Based on model parameters and decision variables, the port planning rule base is quantified into mathematical constraint expressions to obtain a set of constraint conditions; wherein, the set of constraint conditions includes hard constraints and soft constraints; Construct a multi-objective optimization function based on soft constraints and preset optimization objectives; The constraint set and multi-objective optimization function are integrated based on the CSP solution framework to obtain the constrained optimization model; The optimization of the constrained optimization model using a constrained optimization algorithm includes: The business requirement list is prioritized based on a preset business level classification standard to obtain a business requirement sequence. Based on the equipment resource list, the CSP solver is used to search for candidate ports in the business requirement sequence to obtain preliminary port matching results. Conflict detection is performed on the preliminary port matching results based on the set of constraints. If a conflict is detected, a conflict detection result is generated and repaired to obtain a conflict-free matching result; If no conflict is detected, the initial port matching result is marked as a conflict-free matching result; Substitute the conflict-free matching result into the multi-objective optimization function and calculate the function value. Select the allocation scheme corresponding to the minimum function value to obtain the port allocation result.

2. The intelligent planning method for transmission and data network ports on a new railway line according to claim 1, characterized in that, The methods for obtaining the set of constraints include: The mandatory railway design specifications in the port planning rule base are transformed into hard constraints; wherein, the hard constraints include port type matching constraints, primary and backup service pairing configuration constraints, and port uniqueness constraints. The engineering optimization guidance requirements in the port planning rule base are transformed into soft constraints; wherein, the soft constraints include physical isolation constraints for critical services, convergence constraints for service resources, and fragmentation constraints for port allocation; By integrating hard and soft constraints, a set of constraints is obtained.

3. The intelligent planning method for transmission and data network ports on a new railway line according to claim 1, characterized in that, The methods for obtaining the conflict detection results include: Following the principle of prioritizing hard constraints and then soft constraints, the constraint compliance of each port's initial matching result is verified one by one. If a violation of hard constraints is detected, a system alarm is triggered, and the conflict level is marked as severe. If a violation of a soft constraint is detected, the conflict level is marked as "normal" and no alarm is triggered. Record the name of the service in which the conflict occurred, the port location, the type of conflict, and the level of conflict, and generate conflict detection results.

4. The intelligent planning method for transmission and data network ports on a new railway line according to claim 3, characterized in that, The generation and repair of conflict detection results includes: The conflict detection results are classified into hard constraint conflicts and soft constraint conflicts according to the conflict type. For hard constraint conflicts, perform a backtracking operation to allocate low-priority services and enable backup slots for repair. The repair operations for resolving soft constraint conflicts are not critical business operations and involve adjusting port allocation positions; A second constraint check is performed on the repaired matching results. If there are no hard constraint conflicts, a conflict-free matching result is generated; if hard constraint conflicts still exist, insufficient device resources are reported and a prompt to adjust the device configuration is given.

5. The intelligent planning method for transmission and data network ports on a new railway line according to claim 1, characterized in that, The method for obtaining the length of the pigtail includes: Extract optical interface port information from the port allocation results, and associate the measured physical wiring distance between the equipment cabinet and ODF rack corresponding to each optical interface port to generate a detailed list of optical interface ports; Call the formula for calculating pigtail length The system performs batch automated calculations on the detailed list of optical interface ports to obtain the original pigtail lengths; where D is the measured physical cabling distance between the equipment cabinet and the ODF rack. This is the redundancy coefficient. For the length of the container; Based on the pre-established railway communication construction practices, the original pigtail length is standardized to obtain the pigtail length.

6. The intelligent planning method for transmission and data network ports on a new railway line according to claim 5, characterized in that, The method for setting the redundancy coefficient includes: Identify the core and secondary influencing factors; the core influencing factors include cabling complexity, site service level, and fiber optic cable laying method, while the secondary influencing factors include construction technology level, operation and maintenance requirements, and data center environment. The core influencing factors are matched with a preset redundancy coefficient benchmark table to obtain the initial redundancy coefficient. The initial redundancy coefficient is adaptively adjusted based on secondary influencing factors to obtain the candidate redundancy coefficient. The candidate redundancy coefficients are verified for practical engineering adaptability to obtain the redundancy coefficients.

7. The intelligent planning method for transmission and data network ports on a new railway line according to claim 1, characterized in that, The structured encoding and integration of port allocation results and pigtail lengths includes: Based on the preset port system hierarchy, a unique machine-readable code is assigned to the port allocation result and the pigtail length allocation, generating a set of coded planning information. The coded planning information set is integrated with multi-dimensional attributes and the consistency of the information is verified to obtain integrated planning data; wherein, the multi-dimensional attributes include business attributes, equipment attributes and engineering attributes; The integrated planning data is populated into the preset configuration ledger template to obtain the port configuration ledger; the port configuration ledger has also undergone multi-format adaptation processing and supports multi-format export.

8. A new railway transmission and data network port intelligent planning system, applied to the new railway transmission and data network port intelligent planning method as described in any one of claims 1-7, characterized in that, It includes a list acquisition module, a model building module, an optimization solution module, a pigtail calculation module, and a ledger generation module; The list acquisition module is used to acquire the business requirement list and the equipment resource list; The model building module is used to build a constraint optimization model based on a preset port planning rule base. The optimization solution module is used to optimize and solve the constraint optimization model based on the business requirement list and the equipment resource list, and obtain the port allocation result by means of the constraint optimization algorithm; The pigtail calculation module is used to calculate the pigtail length based on the port location and the physical distance between the equipment cabinet and the ODF rack in the port allocation result. The ledger generation module is used to perform structured encoding and integration of port allocation results and pigtail lengths to obtain a port configuration ledger.

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