A power communication network evaluation method based on multi-source data fusion

Abstract

The present invention belongs to the technical field of electric power communication network assessment, and specifically discloses an electric power communication network assessment method based on multi-source data fusion, including: at the logical layer, calculating node connectivity, link robustness, network efficiency, and link redundancy indicators; at the physical layer, calculating actual laying path efficiency, tray margin index, fiber core abundance, common trench risk factor, and common cable vulnerability index; constructing a logical-physical coupling assessment model to identify implicit coupling relationships through matrix analysis; and weightedly integrating the assessment results of the logical layer, physical layer, and coupling layer to output a comprehensive network score and health level. The present invention conducts a multi-dimensional, full-link, and systematic assessment of the operating mode of the electric power communication network, thereby improving the scientific nature of network planning and the targeted nature of operation and maintenance management. The present invention solves the problems of existing electric power communication network assessment methods, which lack consideration of the physical layer and insufficient identification of logical-physical coupling relationships.

Description

Technical Field

[0001] The present invention belongs to the technical field of power communication network evaluation, and in particular relates to a power communication network evaluation method based on multi-source data fusion. Background Art

[0002] As critical infrastructure supporting the safe and stable operation of power systems and the efficient transmission of information, scientific evaluation of the operational status of power communication networks is crucial for improving network planning efficiency, enhancing system reliability, and ensuring high availability. As the digitalization and intelligentization of the power industry accelerate, power communication networks face the challenges of continuously expanding service scale and increasing real-time performance requirements. Traditional evaluation methods are unable to meet the network design and operation and maintenance needs of this new landscape.

[0003] Looking back at the research development process, early domestic and international research focused primarily on the modeling and analysis of network topology structures. Drawing on graph theory and complex network theory, these studies employed metrics such as node degree distribution, connectivity, and path redundancy to assess network functionality, providing theoretical support for the preliminary design and structural optimization of communication networks. While these approaches offer some representativeness at the topological level, they pay less attention to the physical layer factors involved in actual deployments, limiting the applicability of the evaluation results in engineering practice.

[0004] In recent years, the assessment of power communication networks has gradually expanded into areas related to system stability, reliability, and risk control. Some studies have constructed network effectiveness models, fault propagation models, and redundant structure models to simulate network performance changes under different operating conditions, thereby quantifying the system's robustness and invulnerability. At the same time, some scholars have proposed refining evaluation indicators into multiple layers, such as topology, equipment, and operation, to achieve a multi-dimensional comprehensive assessment. However, most of these methods are still limited to logical structural analysis and fail to reflect the true operational status of the network at the physical level. In particular, they lack consideration of key operational and maintenance factors such as optical cable physical routing, co-cable / co-trenching relationships, fiber core redundancy, and pipeline resource utilization. In current practical applications, the operational quality of power communication networks is highly coupled to physical deployment characteristics. The mapping between logical topology and physical paths has a decisive impact on network reliability, maintainability, and scalability. Existing assessment methods often neglect the coupled analysis between the logical and physical layers, resulting in discrepancies between assessment results and actual network operation, which in turn affects the scientific nature of planning schemes and the effectiveness of operation and maintenance strategies. Summary of the Invention

[0005] The purpose of this invention is to solve the problems of insufficient consideration of the physical layer and insufficient identification of the logical-physical coupling relationship in existing power communication network evaluation methods, and proposes a power communication network evaluation method based on multi-source data fusion.

[0006] The technical solution of the present invention is: a power communication network evaluation method based on multi-source data fusion, comprising the following steps:

[0007] Collect logical topology information and physical layer deployment information of the power communication network;

[0008] Based on the logical topology information, at the logical level, node connectivity, link robustness, network efficiency, and link redundancy are calculated;

[0009] The logical evaluation comprehensive score is calculated based on node connectivity, link robustness, network efficiency value and link redundancy;

[0010] Based on the physical layer deployment information, at the physical level, the actual laying path efficiency, tray margin index, fiber core margin, common trench risk factor, and common cable vulnerability index are calculated;

[0011] Calculate the comprehensive physical assessment score based on the actual laying path efficiency, tray margin index, fiber core abundance, common trench risk factor, and common cable vulnerability index;

[0012] Construct a logical redundancy matrix and a physical coupling matrix, and quantify the actual redundancy through matrix analysis to obtain a logical-physical coupling evaluation score;

[0013] The comprehensive score of the logical assessment, the comprehensive score of the physical assessment and the logical-physical coupling assessment score are weighted and summed to obtain the network health score. The health level of the power communication network is judged based on the network health score to complete the power communication network assessment.

[0014] Preferably, the node connectivity is obtained by calculating the average network degree, and the calculation formula of the average network degree is:

[0015]

[0016] in, represents the average degree of the network, that is, the node connectivity, Indicates the total number of network nodes, Indicates the network nodes, Representation node The degree of the node The number of connected links;

[0017] The calculation formula of the link robustness is:

[0018]

[0019] in, Indicates link robustness, represents the total number of edges in the network, represents the measured k-edge connectivity, , measured k-edge connectivity Through Monte Carlo simulation, represents the theoretical maximum connectivity, , Indicates the minimum value, Indicates the natural base Logarithmic function with base ;

[0020] The calculation formula of the network efficiency value is:

[0021]

[0022] in, represents the network efficiency value, Representation node To Node The shortest path hop count, When it approaches 1, it means that the number of hops of the shortest path between all node pairs in the network is close to 1, that is, the network is a fully connected graph and the communication efficiency is the highest; when When it is close to 0, it means that the communication efficiency between nodes in the network is low, and there are many long paths or communication bottlenecks;

[0023] The calculation formula of the link redundancy is:

[0024]

[0025] in, Indicates link redundancy, is the number of redundant links, is the total number of links.

[0026] Preferably, the calculation formula of the logical evaluation comprehensive score is:

[0027]

[0028] in, Represents the comprehensive score of logic evaluation, 、 、 and Represents node connectivity , link robustness coefficient , network efficiency value and link redundancy The weight of .

[0029] Preferably, the calculation formula for the actual paving path efficiency is:

[0030]

[0031] in, Indicates the actual paving path efficiency, represents the total number of paths evaluated, Indicates the The length of the logical design path, Indicates the The actual physical path length;

[0032] The calculation formula of the disk remaining index is:

[0033]

[0034] in, Indicates the disk remaining index, Indicates the redundant unused length of the optical cable. Indicates the total laying length of the optical cable;

[0035] The calculation formula of the core richness is:

[0036]

[0037] in, Indicates the fiber core richness, Indicates the total number of fiber cores in the optical cable. Indicates the number of occupied fiber cores;

[0038] The calculation formula of the common groove risk factor is:

[0039]

[0040] in, represents the common groove risk factor, The length of optical cable laid in the common trench is is the total length of the laid optical cable, is the channel type risk weight;

[0041] The calculation formula of the cable vulnerability index is:

[0042]

[0043] in, represents the cable vulnerability index, Indicates the number of links sharing the same cable, Indicates the total number of logical links. represents the natural base, represents the length influence coefficient, Indicates the maximum continuous length of the common cable segment.

[0044] Preferably, the calculation formula for the physical assessment comprehensive score is:

[0045]

[0046] in, represents the comprehensive score of physical assessment, Indicates the actual paving path efficiency, Indicates the disk remaining index, Indicates the fiber core richness, represents the common groove risk factor after normalization, represents the normalized common cable vulnerability index, 、 、 、 and Represents the actual paving path efficiency , disk balance index , Network fiber core abundance , Common groove risk factor after normalization and the normalized cable vulnerability index The weight of .

[0047] As an example, the normalized common groove risk factor is The calculation formula is:

[0048]

[0049] in, represents the hyperbolic tangent function;

[0050] By co-channel risk factor Normalization processing is performed to achieve a negative correlation between the larger the risk value and the lower the standardized score.

[0051] As an advantage, the normalized cable vulnerability index is The calculation formula is:

[0052]

[0053] By normalizing the common cable vulnerability index, we focus on monitoring the medium and low risk ranges to avoid excessive sensitivity in the high value ranges.

[0054] Preferably, the calculation formula of the real redundancy is:

[0055]

[0056] in, represents the true redundancy, , represents the logical-physical coupling evaluation score, Represents the logical redundancy matrix, the logical redundancy matrix Elements in Indicates that there is a redundant link. Represents the physical coupling matrix, the physical coupling matrix Elements in Indicates a link and Link There is a common trench / cable, represents the Hadamard product, Represents the matrix Frobenius norm.

[0057] Preferably, the network health score is calculated as follows:

[0058]

[0059] in, represents the network health score, Indicates the comprehensive score of logic evaluation The weight of Indicates the comprehensive score of physical assessment The weight of Indicates the logical-physical coupling evaluation score The weight of .

[0060] Preferably, the health level of the power communication network is determined according to the network health score as follows:

[0061] The power communication network health level is excellent, and the network health score is: ;

[0062] The health level of the power communication network is good, and the network health score is: ;

[0063] The power communication network health level is qualified, and the network health score is: ;

[0064] The health level of the power communication network is sub-healthy, and the network health score is: ;

[0065] The health level of the power communication network is risky, and the network health score is: .

[0066] The beneficial effects of the present invention are:

[0067] 1. This invention incorporates the logical topology structure, physical path information, and their coupling relationships of the power communication network into a unified evaluation framework, breaking through the limitations of traditional methods that only start from the business layer or logical layer. It achieves multi-dimensional, multi-level, and full-factor coverage of the network operation status, significantly improving the comprehensiveness and accuracy of the evaluation.

[0068] 2. This invention accurately reflects the engineering status of the power communication network in actual deployment and operation and maintenance by evaluating key physical layer indicators such as actual routing efficiency, tray margin, fiber core richness, common trench risk, and common cable vulnerability. It makes up for the problem of insufficient understanding of the physical layer in existing methods and enhances the model's guidance ability in actual network construction and optimization.

[0069] 3. This invention builds a redundancy validity verification model to quantify the inconsistencies and potential risks between the logical topology and the physical path, effectively identifying the hidden dangers of "redundancy failure" caused by shared cables and trenches, and improving the accuracy and depth of fault tolerance analysis of power communication networks.

[0070] 4. The evaluation method of the present invention has a strong engineering implementation foundation and can be rapidly deployed in combination with existing GIS systems, optical fiber resource databases, network topology maps and other data platforms. It is applicable to multiple business links such as planning and design, expansion and upgrading, operation monitoring and fault diagnosis of power communication networks, and has good versatility and promotion prospects. BRIEF DESCRIPTION OF THE DRAWINGS

[0071] Figure 1 The figure shows a power communication network evaluation method based on multi-source data fusion provided in Example 1 of the present invention.

[0072] Figure 2 The figure shows a schematic diagram of the topological logic and physical path matching of a power communication subnet in a certain city provided in Example 2 of the present invention. DETAILED DESCRIPTION

[0073] The exemplary embodiments of the present invention will now be described in detail with reference to the accompanying drawings. It should be understood that the embodiments shown and described in the accompanying drawings are merely exemplary and are intended to illustrate the principles and spirit of the present invention, rather than to limit the scope of the present invention.

[0074] Example 1:

[0075] like Figure 1 As shown, a power communication network evaluation method based on multi-source data fusion includes the following steps:

[0076] S1. Collect the logical topology information and physical layer deployment information of the power communication network;

[0077] S2. Based on the logical topology information, calculate node connectivity, link robustness, network efficiency, and link redundancy at the logical level.

[0078] S3. Calculate a comprehensive logical evaluation score based on node connectivity, link robustness, network efficiency, and link redundancy.

[0079] S4. Based on the physical layer deployment information, calculate the actual laying path efficiency, tray margin index, fiber core margin, common trench risk factor, and common cable vulnerability index at the physical layer.

[0080] S5. Calculate the overall physical assessment score based on the actual laying path efficiency, reel margin index, fiber core margin, common trench risk factor, and common cable vulnerability index;

[0081] S6. Construct a logical redundancy matrix and a physical coupling matrix, and quantify the actual redundancy through matrix analysis to obtain a logical-physical coupling evaluation score;

[0082] S7. Take a weighted sum of the logical assessment comprehensive score, the physical assessment comprehensive score, and the logical-physical coupling assessment score to obtain a network health score, and determine the health level of the power communication network based on the network health score to complete the power communication network assessment.

[0083] In this embodiment, the logical topology of the power communication network is modeled, and indicators such as node connectivity, link robustness, network efficiency, and link redundancy are calculated to reflect the connectivity, stability, and transmission performance of the logical structure.

[0084] In this embodiment, the balance of node connections, i.e., node connectivity, is evaluated by calculating the average network degree. Node connectivity can effectively eliminate the impact of network scale differences on the evaluation results. A higher average degree indicates a greater number of links connected to each node in the network, a stronger overall network connectivity, and a higher efficiency in information transfer between nodes. The calculation formula is:

[0085]

[0086] in, represents the average degree of the network, Indicates the total number of network nodes, Indicates the network nodes, Representation node First, traverse all nodes, count the number of connected links for each node, then sum the degrees of all nodes, and then divide the sum by the total number of network nodes. , and get the average degree of the network.

[0087] In this embodiment, link robustness is used to evaluate whether the network is still connected after a certain number of links are removed. In order to improve the efficiency of connectivity evaluation of large-scale networks, the embodiment of the present invention uses Monte Carlo simulation to replace the traditional exhaustive method. Compared with the high computational cost of traversing the link removal combinations one by one, the Monte Carlo method performs multiple simulations to approximate the actual results by randomly sampling some link removal scenarios. This method significantly reduces the computational complexity while ensuring the evaluation accuracy, and is particularly suitable for the rapid evaluation of large-scale power communication networks. The link robustness calculation formula is:

[0088]

[0089] in, Indicates link robustness, represents the total number of edges in the network, represents the measured k-edge connectivity, (obtained through Monte Carlo simulation), represents the theoretical maximum connectivity, , Indicates the natural base Logarithmic function with base ;

[0090] The specific process of Monte Carlo algorithm simulation is as follows:

[0091] Initialization: Set removal counter m=0;

[0092] Loop test: Randomly select an unmarked link; virtually remove the link and check network connectivity; if connectivity remains, set the removal counter m+1=1; otherwise, record k=m+1;

[0093] Monte Carlo optimization: Repeat the loop test for 1000 samples, take the mode value of k, and obtain the measured k-edge connectivity.

[0094] In this embodiment, the network efficiency value evaluation is currently intended to be evaluated at the logical level, using the shortest path hop count to evaluate the efficiency of the power communication network. Specifically, the network efficiency evaluation reflects the ease of communication between all node pairs in the network. First, the node is calculated using Dijkstra (or other shortest path algorithms). arrive The shortest path hop count , then sum the communication efficiencies of all node pairs and finally normalize them. The specific calculation formula is:

[0095]

[0096] in, represents the network efficiency value, Indicates the total number of network nodes; When it approaches 1, it means that the number of hops of the shortest path between all pairs of nodes in the network is close to 1, that is, the network is a fully connected graph and the communication efficiency is the highest. When it is close to 0, it means that the communication efficiency between nodes in the network is low, and there may be more long paths or communication bottlenecks.

[0097] In this embodiment, link redundancy is an important indicator for measuring the resilience and reliability of the power communication network. Checking whether each link has an alternative backup link helps assess the network's connectivity and recovery capabilities in the event of a failure or attack. A high link redundancy means that even if a link fails, a viable communication path still exists, ensuring that critical data can be transmitted without affecting the normal operation of the power dispatch and control system. It is calculated using the following formula:

[0098]

[0099] in, Indicates link redundancy, is the number of redundant links (identifiable through topology analysis), is the total number of links.

[0100] In this embodiment, the logical evaluation comprehensive score reflects the overall performance of the logical layer by quantifying the connectivity, robustness, and efficiency characteristics of the network topology. It integrates four core indicators: node connectivity, link robustness, network efficiency, and link redundancy, and distributes weights evenly to fully characterize the topological characteristics. The calculation formula is:

[0101]

[0102] in, Indicates the comprehensive score of the power communication network logic level, Represents the logical level, 、 、 and Represent the node connectivity index (normalized value, range [0, 1]), link robustness coefficient (normalized value, range [0, 1]), network efficiency value (normalized value, range [0, 1]) and link redundancy (normalized value, range [0, 1]) weights, there are .

[0103] In this embodiment, based on the GIS system and on-site measurement data, operation and maintenance related indicators such as actual laying path efficiency, tray margin, fiber core surplus, common trench risk factor and common cable vulnerability are extracted to evaluate the rationality and physical stability of the actual deployment path.

[0104] In this embodiment, the efficiency of the actual paving path is evaluated by calculating the difference between the topological straight-line distance between nodes and the actual paving length. If the difference is too large, it will result in higher paving costs. At the same time, longer running lines will reduce network efficiency. The specific calculation formula is:

[0105]

[0106] in, Indicates the actual paving path efficiency index, represents the total number of paths evaluated, Indicates the The length of the logical design path (km, taken from the network planning drawings), Indicates the Actual physical trail length (km, on-site measurement value).

[0107] In this embodiment, fiber slack is primarily used to measure the redundancy and physical reliability of fiber paths. Sufficient fiber slack ensures rapid communication restoration in the event of a network failure, improving network reliability and robustness. The fiber slack index is calculated as follows:

[0108]

[0109] in, Indicates disk remainder index, total length Indicates the redundant unused length of the optical cable (m, measured at the splice box), and the remaining length of the reel Indicates the total laying length of the optical cable (m, project acceptance data).

[0110] In this embodiment, the fiber core surplus reflects the remaining available fiber cores in the optical cable, which directly determines the feasibility of future business growth or new links. In the event of a link failure or fiber damage, the spare fiber core can be used for rapid switching, ensuring uninterrupted communication and improving the robustness of the power communication network. The calculation formula is:

[0111]

[0112] in, Indicates the fiber core abundance and the total number of fiber cores : Total number of fiber cores in the cable (standard value: 24 / 48 / 96), used fiber cores : Number of occupied fiber cores (OTDR test results).

[0113] In this example, the co-trench risk factor measures the potential risk when optical cables in a power and telecommunications network share the same trench with other power, telecommunications, or municipal facilities. If optical cables share a trench with high-voltage cables or gas pipelines, external construction, natural disasters, or equipment failures could simultaneously affect multiple critical infrastructures, leading to cascading failures. The calculation formula is:

[0114]

[0115] in, represents the common groove risk factor, The length of optical cables laid in the common trench (km, calculated by GIS system), is the total length of optical cables laid (km, GIS system statistics), is the risk weight of the channel type (direct buried: 1.0, pipeline: 0.6, overhead: 0.3).

[0116] For the above common groove risk factors Normalization is performed to achieve a negative correlation between the higher the risk value and the lower the standardized score. When the proportion of common groove length is greater than 30%, the score drops rapidly. When the proportion of common groove length is less than 10%, the score changes slowly, retaining a reasonable degree of differentiation for low-risk areas. The normalization formula is:

[0117]

[0118] in, represents the common groove risk factor after normalization, represents the hyperbolic tangent function.

[0119] In this embodiment, the cable vulnerability index measures the overall vulnerability of multiple communication links sharing the same optical cable, namely the risk of single point of failure (SPOF). If a single optical cable carries multiple critical communication links, cable damage may cause widespread communication disruption, affecting grid scheduling and control. The calculation formula is:

[0120]

[0121] in, represents the cable vulnerability index, Indicates the number of links sharing the same cable, Indicates the total number of logical links. represents the natural base, Indicates the length influence coefficient (default 0.02 / km), Indicates the maximum continuous length of the common cable section (km).

[0122] For the above cable vulnerability index Normalization is performed: In actual networks, co-cable length typically follows a long-tail distribution (with a few links experiencing significant co-cable contamination). By standardizing the monitoring, we focus on monitoring low- and medium-risk intervals, avoiding oversensitivity in high-value intervals. This ensures that highly co-cabled links do not completely dominate the assessment results.

[0123]

[0124] in, represents the normalized cable vulnerability index.

[0125] In this embodiment, in the physical level evaluation method, the weighted addition method similar to the logical level is used for calculation. However, unlike the previous equal weighting process, different weights are given to each indicator to obtain the comprehensive score of the physical evaluation. :

[0126]

[0127] in, 、 、 、 and Represents the actual paving path efficiency , disk balance index , Network fiber core abundance , Common groove risk factor after normalization and the normalized cable vulnerability index Compared to the common trench risk factor and common cable vulnerability index, which affect network security, indicators related to maintenance and scalability (such as physical length, fiber core margin, and reel margin) are given lower weights. This weighting reflects the priority of network security to ensure that the assessment results are more in line with actual needs, focusing on factors that have a greater impact on the stability of power and telecommunications networks. For example: = = =0.1, and = =0.35.

[0128] In this embodiment, the path matching degree and redundancy validity matrix analysis are used to identify the common trench / cable problem of logical links in physical deployment, prevent the occurrence of "false redundancy", and ensure the authenticity and reliability of the assessment.

[0129] Traditional redundancy assessment methods for power communication networks typically assume that redundant links at the logical layer are completely independent at the physical layer. This means that if multiple paths exist in the logical topology, the network is considered highly redundant. However, in reality, the physical layer may share common trenches (sharing the same pipe) or cables (sharing the same optical cable). This can cause seemingly independent logical links to be implicitly coupled at the physical layer.

[0130] When a physical layer failure occurs, these shared trench / cable links may fail simultaneously, leading to a "false redundancy" problem. This means that the redundant links at the logical layer do not actually provide true independent backup capabilities. Matrix analysis identifies implicit coupling, resolving the "false redundancy" misjudgment problem in traditional methods. We construct a logical redundancy matrix and a physical coupling matrix, respectively, and use matrix analysis to quantify true redundancy:

[0131]

[0132] in, represents the true redundancy, Represents the logical redundancy matrix (N×N), the logical redundancy matrix Elements in Indicates a link and There is redundancy in the logical topology (such as backup paths). Indicates that there is no redundant relationship. Represents the physical coupling matrix (N×N), the physical coupling matrix Elements in Indicates a link and Link There is a common trench / cable, represents the Hadamard product, represents the matrix Frobenius norm where, represents the true redundancy, represents the logical redundancy matrix (N×N), Indicates that there is a redundant link. represents the physical coupling matrix (N×N), Indicates that links i and j share a common trench / cable. Represents the Hadamard product (element-wise multiplication), identifying the case where the links are both redundant and physically coupled. represents the Frobenius norm of the matrix, which is the square root of the sum of the squares of all the elements of the matrix. It measures the "total strength" or "total number". The real redundancy measures how much of the logical redundant links are "contaminated", that is, they are not actually independent at the physical layer. Therefore, the real redundancy is a normalized indicator between [0,1], that is, , Indicates the logical-physical coupling evaluation score.

[0133] In this embodiment, the evaluation results of the logical layer, physical layer, and coupling layer are standardized and assigned corresponding weights, ultimately forming a comprehensive evaluation score of the network operation mode.

[0134] The embodiment of the present invention adopts a three-layer weighted average model to integrate the evaluation results of the three dimensions of logic, physics, and coupling according to preset weights to obtain a network health score. :

[0135]

[0136] in, represents the network health score, Indicates the comprehensive score of logic evaluation The weight of Indicates the comprehensive score of physical assessment The weight of Indicates the logical-physical coupling evaluation score The weight of .

[0137] The weight distribution is shown in Table 1.

[0138] Table 1 Weight distribution

[0139]

[0140] Assessment grading and O&M recommendation output: The network health status is graded based on the total assessment score, and corresponding inspection cycles, optimization directions, and rectification recommendations are output to assist in the refined O&M of the power communication network. The specific grading standards are shown in Table 2.

[0141] Table 2 Grading standards

[0142]

[0143] Example 2:

[0144] Based on Example 1, this embodiment of the present invention uses a typical power communication subnetwork in a certain city as an example to conduct a comprehensive evaluation test to verify the applicability of the present invention method in actual scenarios. This power communication subnetwork includes several key communication nodes and links, has a certain scale and structural complexity, and is representative. The relevant network data sources include:

[0145] Network topology information is derived from the NetworkX library modeling;

[0146] The actual physical path information comes from the GIS platform and construction acceptance data;

[0147] Operation and maintenance indicator data, including optical cable path, reel length, fiber core usage, etc.

[0148] like Figure 2 As shown in the figure, the logical topology of the power communication subnet and the actual physical path are superimposed:

[0149] The red dotted line indicates the physical path formed during actual construction, which deviates from the planned path to a certain extent;

[0150] The orange area is marked as a common cable section, indicating that multiple logical links share the same cable;

[0151] The blue area is marked as a shared trench segment, indicating that the links share the same physical trench.

[0152] Taking nodes N16 to N12 as an example, although there are two paths at the logical layer, they share a common optical cable at the physical layer, which poses a potential single point failure risk. For another example, N14 to N18 shares a channel with the starting section of N14 to N19, indicating that they have common risk conduction when encountering external damage.

[0153] Next, according to the logic layer evaluation method of the present invention, the following indicators are calculated for the sub-network:

[0154] Node connectivity quantification: Based on the average number of connections between network nodes, a score of 0.50 was calculated;

[0155] Link robustness index: calculated using the minimum edge connectivity normalization method, with a score of 0.10;

[0156] Network efficiency index: calculated as the reciprocal mean of the shortest path hop counts between all node pairs, with a normalized score of 0.98;

[0157] Link redundancy index: Calculated by normalizing the number of redundant links in the minimum spanning tree, the score is 0.53.

[0158] The four indicators were weighted using equal weight (0.25), and the overall score of the logical topology layer was obtained as follows:

[0159]

[0160] At the physical level, the assessment is conducted from three dimensions: routing efficiency, resource redundancy, and risk factors:

[0161] Actual path efficiency: The average score of the ratio of logical to physical path length is 0.473;

[0162] The disk remainder index: after calculating the disk remainder length and normalizing it, the score is 0.326;

[0163] Fiber core abundance: calculated based on the percentage of remaining fiber cores in the cable, with a score of 0.741;

[0164] Common groove risk factor: The score after weighting the groove type and the common groove ratio is 0.459;

[0165] The common cable vulnerability index is calculated based on the number and length of common cable links, with a score of 0.648.

[0166] Using a weighted strategy (w5=w6=w7=0.1, w8=w9=0.35), the comprehensive score of the physical layer is calculated as:

[0167]

[0168] To evaluate the potential implicit coupling and failure correlation of logical links in physical deployment, a logical redundancy matrix and a physical coupling matrix are constructed. The Hadamard product and Frobenius norm methods are used to calculate the true redundancy. The resulting coupling layer comprehensive score is:

[0169]

[0170] Based on the evaluation results of the above three dimensions, the final network health score is calculated according to the multi-dimensional weighted fusion model proposed in this invention:

[0171]

[0172] According to the grading standards set by this invention, the score corresponds to the assessment level of "sub-health". It is recommended that the operation and maintenance unit implement the following measures:

[0173] Reconfigure links in concentrated cable areas (such as N16–N12) to avoid potential single points of failure.

[0174] Optimize the design of common trench sections to improve fault isolation capabilities;

[0175] Increase disk redundancy and fiber core configuration of key links to enhance network recoverability.

[0176] Those skilled in the art will appreciate that the embodiments described herein are intended to help readers understand the principles of the present invention, and it should be understood that the scope of protection of the present invention is not limited to such specific descriptions and embodiments. Those skilled in the art can make various other specific variations and combinations based on the technical teachings disclosed in the present invention without departing from the essence of the present invention, and such variations and combinations are still within the scope of protection of the present invention.

Claims

1. A power communication network evaluation method based on multi-source data fusion, characterized in that: The following steps are involved: Collect logical topology information and physical layer deployment information of the power communication network; Based on the logical topology information, at the logical level, node connectivity, link robustness, network efficiency, and link redundancy are calculated; The logical evaluation comprehensive score is calculated based on node connectivity, link robustness, network efficiency value and link redundancy; Based on the physical layer deployment information, at the physical level, the actual laying path efficiency, tray margin index, fiber core margin, common trench risk factor, and common cable vulnerability index are calculated; Calculate the comprehensive physical assessment score based on the actual laying path efficiency, tray margin index, fiber core abundance, common trench risk factor, and common cable vulnerability index; Construct a logical redundancy matrix and a physical coupling matrix, and quantify the actual redundancy through matrix analysis to obtain a logical-physical coupling evaluation score; The calculation formula for true redundancy is: in, represents the true redundancy, , represents the logical-physical coupling evaluation score, Represents the logical redundancy matrix, the logical redundancy matrix Elements in Indicates that there is a redundant link. Represents the physical coupling matrix, the physical coupling matrix Elements in Indicates a link and Link There is a common trench / cable, represents the Hadamard product, represents the matrix Frobenius norm; The comprehensive score of the logical assessment, the comprehensive score of the physical assessment and the logical-physical coupling assessment score are weighted and summed to obtain the network health score. The health level of the power communication network is judged based on the network health score to complete the power communication network assessment.

2. The electric power communication network evaluation method based on multi-source data fusion according to claim 1 is characterized in that: The node connectivity is obtained by calculating the average network degree. The calculation formula of the average network degree is: in, represents the average degree of the network, that is, the node connectivity, Indicates the total number of network nodes, Indicates the network nodes, Representation node The degree of the node The number of connected links; The calculation formula of the link robustness is: in, Indicates link robustness, represents the total number of edges in the network, represents the measured k-edge connectivity, , measured k-edge connectivity Through Monte Carlo simulation, represents the theoretical maximum connectivity, , Indicates the minimum value, Indicates the natural base Logarithmic function with base ; The calculation formula of the network efficiency value is: in, represents the network efficiency value, Representation node To Node The shortest path hop count, When it approaches 1, it means that the number of hops of the shortest path between all node pairs in the network is close to 1, that is, the network is a fully connected graph and the communication efficiency is the highest; when When it is close to 0, it means that the communication efficiency between nodes in the network is low, and there are many long paths or communication bottlenecks; The calculation formula of the link redundancy is: in, Indicates link redundancy, is the number of redundant links, is the total number of links.

3. The electric power communication network evaluation method based on multi-source data fusion according to claim 1 is characterized in that: The calculation formula of the logical evaluation comprehensive score is: in, Represents the comprehensive score of logic evaluation, 、 、 and Represents node connectivity , link robustness coefficient , network efficiency value and link redundancy The weight of .

4. The electric power communication network evaluation method based on multi-source data fusion according to claim 1 is characterized in that: The calculation formula for the actual paving path efficiency is: in, Indicates the actual paving path efficiency, represents the total number of paths evaluated, Indicates the The length of the logical design path, Indicates the The actual physical path length; The calculation formula of the disk remaining index is: in, Indicates the disk remaining index, Indicates the redundant unused length of the optical cable. Indicates the total laying length of the optical cable; The calculation formula of the core richness is: in, Indicates the fiber core richness, Indicates the total number of fiber cores in the optical cable. Indicates the number of occupied fiber cores; The calculation formula of the common groove risk factor is: in, represents the common groove risk factor, The length of optical cable laid in the common trench is is the total length of the laid optical cable, is the channel type risk weight; The calculation formula of the cable vulnerability index is: in, represents the cable vulnerability index, Indicates the number of links sharing the same cable, Indicates the total number of logical links. represents the natural base, represents the length influence coefficient, Indicates the maximum continuous length of the common cable segment.

5. The electric power communication network evaluation method based on multi-source data fusion according to claim 4 is characterized in that: The calculation formula for the physical assessment comprehensive score is: in, represents the comprehensive score of physical assessment, Indicates the actual paving path efficiency, Indicates the disk remaining index, Indicates the fiber core richness, represents the common groove risk factor after normalization, represents the normalized common cable vulnerability index, 、 、 、 and Represents the actual paving path efficiency , disk balance index , Network fiber core abundance , Common groove risk factor after normalization and the normalized cable vulnerability index The weight of .

6. The electric power communication network evaluation method based on multi-source data fusion according to claim 5 is characterized in that: The normalized common groove risk factor The calculation formula is: in, represents the hyperbolic tangent function; By co-channel risk factor Normalization processing is performed to achieve a negative correlation between the larger the risk value and the lower the standardized score.

7. The electric power communication network evaluation method based on multi-source data fusion according to claim 5 is characterized in that: The normalized cable vulnerability index The calculation formula is: By normalizing the common cable vulnerability index, we focus on monitoring the medium and low risk ranges to avoid excessive sensitivity in the high value ranges.

8. The electric power communication network evaluation method based on multi-source data fusion according to claim 1 is characterized in that: The network health score is calculated as follows: in, represents the network health score, Indicates the comprehensive score of logic evaluation The weight of Indicates the comprehensive score of physical assessment The weight of Indicates the logical-physical coupling evaluation score The weight of .

9. The electric power communication network evaluation method based on multi-source data fusion according to claim 8 is characterized in that: The specific method of judging the health level of the power communication network based on the network health score is as follows: The power communication network health level is excellent, and the network health score is: ; The health level of the power communication network is good, and the network health score is: ; The power communication network health level is qualified, and the network health score is: ; The health level of the power communication network is sub-healthy, and the network health score is: ; The health level of the power communication network is risky, and the network health score is: .

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